RWeka: Interfaces for RWeka package for data science pipelines.
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to RWeka functions that can be used
in a pipeline implemented by magrittr.
Usage
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ntbt_AdaBoostM1(data, ...)
ntbt_Bagging(data, ...)
ntbt_CostSensitiveClassifier(data, ...)
ntbt_DecisionStump(data, ...)
ntbt_Discretize(data, ...)
ntbt_GainRatioAttributeEval(data, ...)
ntbt_IBk(data, ...)
ntbt_InfoGainAttributeEval(data, ...)
ntbt_J48(data, ...)
ntbt_JRip(data, ...)
ntbt_LBR(data, ...)
ntbt_LogitBoost(data, ...)
ntbt_LinearRegression(data, ...)
ntbt_LMT(data, ...)
ntbt_Logistic(data, ...)
ntbt_M5P(data, ...)
ntbt_M5Rules(data, ...)
ntbt_MultiBoostAB(data, ...)
ntbt_Normalize(data, ...)
ntbt_OneR(data, ...)
ntbt_PART(data, ...)
ntbt_SMO(data, ...)
ntbt_Stacking(data, ...)
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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## Not run:
library(intubate)
library(magrittr)
library(RWeka)
## R/Weka Attribute Evaluators
## Original function to interface
GainRatioAttributeEval(Species ~ . , data = iris)
InfoGainAttributeEval(Species ~ . , data = iris)
## The interface puts data as first parameter
ntbt_GainRatioAttributeEval(iris, Species ~ .)
ntbt_InfoGainAttributeEval(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_GainRatioAttributeEval(Species ~ .)
iris %>%
ntbt_InfoGainAttributeEval(Species ~ .)
## R/Weka Classifier Functions
data(infert)
infert$STATUS <- factor(infert$case, labels = c("control", "case"))
## Original function to interface
LinearRegression(weight ~ feed, data = chickwts)
Logistic(STATUS ~ spontaneous + induced, data = infert)
SMO(Species ~ ., data = iris, control = Weka_control(K = list("RBFKernel", G = 2)))
## The interface puts data as first parameter
ntbt_LinearRegression(chickwts, weight ~ feed)
ntbt_Logistic(infert, STATUS ~ spontaneous + induced)
ntbt_SMO(iris, Species ~ ., control = Weka_control(K = list("RBFKernel", G = 2)))
## so it can be used easily in a pipeline.
chickwts %>%
ntbt_LinearRegression(weight ~ feed)
infert %>%
ntbt_Logistic(STATUS ~ spontaneous + induced)
iris %>%
ntbt_SMO(Species ~ ., control = Weka_control(K = list("RBFKernel", G = 2)))
## R/Weka Lazy Learners
## No examples provided. LBR seems to need 'lazyBayesianRules'
## and I am too lazy myself to install it
ntbt_IBk(chickwts, weight ~ feed) ## Example may not make sense
## R/Weka Meta Learners
## MultiBoostAB needs Weka package 'multiBoostAB'
## CostSensitiveClassifier throws an error
## Original function to interface
AdaBoostM1(Species ~ ., data = iris, control = Weka_control(W = "DecisionStump"))
Bagging(Species ~ ., data = iris, control = Weka_control())
LogitBoost(Species ~ ., data = iris, control = Weka_control())
Stacking(Species ~ ., data = iris, control = Weka_control())
## The interface puts data as first parameter
ntbt_AdaBoostM1(iris, Species ~ ., control = Weka_control(W = "DecisionStump"))
ntbt_Bagging(iris, Species ~ ., control = Weka_control())
ntbt_LogitBoost(iris, Species ~ ., control = Weka_control())
ntbt_Stacking(iris, Species ~ ., control = Weka_control())
## so it can be used easily in a pipeline.
iris %>%
ntbt_AdaBoostM1(Species ~ ., control = Weka_control(W = "DecisionStump"))
iris %>%
ntbt_Bagging(Species ~ ., control = Weka_control())
iris %>%
ntbt_LogitBoost(Species ~ ., control = Weka_control())
iris %>%
ntbt_Stacking(Species ~ ., control = Weka_control())
## R/Weka Rule Learners
## Original function to interface
JRip(Species ~ ., data = iris)
M5Rules(mpg ~ ., data = mtcars)
OneR(Species ~ ., data = iris)
PART(Species ~ ., data = iris)
## The interface puts data as first parameter
ntbt_JRip(iris, Species ~ .)
ntbt_M5Rules(mtcars, mpg ~ .)
ntbt_OneR(iris, Species ~ .)
ntbt_PART(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_JRip(Species ~ .)
mtcars %>%
ntbt_M5Rules(mpg ~ .)
iris %>%
ntbt_OneR(Species ~ .)
iris %>%
ntbt_PART(Species ~ .)
## R/Weka Classifier Trees
DF3 <- read.arff(system.file("arff", "cpu.arff", package = "RWeka"))
DF4 <- read.arff(system.file("arff", "weather.arff", package = "RWeka"))
## Original function to interface
DecisionStump(play ~ ., data = DF4)
J48(Species ~ ., data = iris)
LMT(play ~ ., data = DF4)
M5P(class ~ ., data = DF3)
## The interface puts data as first parameter
ntbt_DecisionStump(DF4, play ~ .)
ntbt_J48(iris, Species ~ .)
ntbt_LMT(DF4, play ~ .)
ntbt_M5P(DF3, class ~ .)
## so it can be used easily in a pipeline.
DF4 %>%
ntbt_DecisionStump(play ~ .)
iris %>%
ntbt_J48(Species ~ .)
DF4 %>%
ntbt_LMT(play ~ .)
DF3 %>%
ntbt_M5P(class ~ .)
## R/Weka Filters
w <- read.arff(system.file("arff","weather.arff", package = "RWeka"))
## Original function to interface
Discretize(play ~., data = w)
Normalize(~., data = w)
## The interface puts data as first parameter
ntbt_Discretize(w, play ~.)
ntbt_Normalize(w, ~.)
## so it can be used easily in a pipeline.
w %>%
ntbt_Discretize(play ~.)
w %>%
ntbt_Normalize(~.)
## End(Not run)
Example output
OpenJDK 64-Bit Server VM warning: Can't detect primordial thread stack location - find_vma failed
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.3811740 0.2472972 0.7339960 0.8713692
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.6982615 0.3855963 1.4180030 1.3784027
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.3811740 0.2472972 0.7339960 0.8713692
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.6982615 0.3855963 1.4180030 1.3784027
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.3811740 0.2472972 0.7339960 0.8713692
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.6982615 0.3855963 1.4180030 1.3784027
Linear Regression Model
weight =
73.4538 * feed=linseed,soybean,meatmeal,casein,sunflower +
43.2552 * feed=meatmeal,casein,sunflower +
49.3409 * feed=casein,sunflower +
160.2
Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
Class
Variable control
======================
spontaneous -1.1972
induced -0.4181
Intercept 1.7078
Odds Ratios...
Class
Variable control
======================
spontaneous 0.302
induced 0.6583
SMO
Kernel used:
RBF Kernel: K(x,y) = exp(-2.0*(x-y)^2)
Classifier for classes: setosa, versicolor
BinarySMO
- 1 * < 0.222222 0.541667 0.118644 0.166667> * X]
- 0.3223 * < 0.388889 1 0.084746 0.125> * X]
+ 1 * < 0.222222 0.208333 0.338983 0.416667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
+ 1 * < 0.166667 0.166667 0.389831 0.375> * X]
- 0.265 * < 0.194444 0.416667 0.101695 0.041667> * X]
+ 0.3856 * < 0.194444 0.125 0.389831 0.375> * X]
+ 0.5122 * < 0.75 0.5 0.627119 0.541667> * X]
- 0.525 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.2145 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 0.0262
Number of support vectors: 10
Number of kernel evaluations: 1842 (65.749% cached)
Classifier for classes: setosa, virginica
BinarySMO
1 * < 0.166667 0.208333 0.59322 0.666667> * X]
- 0.8549 * < 0.388889 1 0.084746 0.125> * X]
+ 0.651 * < 1 0.75 0.915254 0.791667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
- 0.1612 * < 0.222222 0.541667 0.118644 0.166667> * X]
+ 0.1957 * < 0.472222 0.083333 0.677966 0.583333> * X]
- 0.1255 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.295 * < 0.944444 0.25 1 0.916667> * X]
+ 0.0856
Number of support vectors: 8
Number of kernel evaluations: 2113 (69.684% cached)
Classifier for classes: versicolor, virginica
BinarySMO
1 * < 0.555556 0.208333 0.677966 0.75> * X]
- 1 * < 0.305556 0.416667 0.59322 0.583333> * X]
- 1 * < 0.666667 0.458333 0.627119 0.583333> * X]
- 1 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 1 * < 0.444444 0.416667 0.694915 0.708333> * X]
- 1 * < 0.527778 0.083333 0.59322 0.583333> * X]
+ 0.3452 * < 1 0.75 0.915254 0.791667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
- 1 * < 0.472222 0.291667 0.694915 0.625> * X]
+ 0.7325 * < 0.555556 0.375 0.779661 0.708333> * X]
- 1 * < 0.666667 0.416667 0.677966 0.666667> * X]
- 0.2861 * < 0.75 0.5 0.627119 0.541667> * X]
+ 1 * < 0.611111 0.416667 0.762712 0.708333> * X]
- 1 * < 0.5 0.375 0.627119 0.541667> * X]
- 1 * < 0.722222 0.458333 0.661017 0.583333> * X]
+ 1 * < 0.472222 0.083333 0.677966 0.583333> * X]
+ 1 * < 0.583333 0.458333 0.762712 0.708333> * X]
+ 1 * < 0.611111 0.5 0.694915 0.791667> * X]
+ 1 * < 0.5 0.416667 0.661017 0.708333> * X]
- 1 * < 0.694444 0.333333 0.644068 0.541667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
+ 1 * < 0.527778 0.333333 0.644068 0.708333> * X]
- 1 * < 0.444444 0.5 0.644068 0.708333> * X]
+ 1 * < 0.5 0.25 0.779661 0.541667> * X]
+ 0.5817 * < 0.805556 0.5 0.847458 0.708333> * X]
+ 1 * < 0.555556 0.291667 0.661017 0.708333> * X]
+ 0.1465 * < 0.361111 0.333333 0.661017 0.791667> * X]
- 1 * < 0.555556 0.208333 0.661017 0.583333> * X]
- 1 * < 0.555556 0.125 0.576271 0.5> * X]
+ 1 * < 0.555556 0.333333 0.694915 0.583333> * X]
+ 1 * < 0.166667 0.208333 0.59322 0.666667> * X]
+ 1 * < 0.805556 0.416667 0.813559 0.625> * X]
- 1 * < 0.555556 0.541667 0.627119 0.625> * X]
+ 1 * < 0.472222 0.416667 0.644068 0.708333> * X]
- 1 * < 0.361111 0.416667 0.59322 0.583333> * X]
- 0.7282 * < 0.583333 0.5 0.59322 0.583333> * X]
- 0.7915 * < 0.333333 0.125 0.508475 0.5> * X]
- 1 * < 0.472222 0.375 0.59322 0.583333> * X]
- 1 * < 0.611111 0.333333 0.610169 0.583333> * X]
+ 0.0916
Number of support vectors: 39
Number of kernel evaluations: 3726 (76.576% cached)
Linear Regression Model
weight =
73.4538 * feed=linseed,soybean,meatmeal,casein,sunflower +
43.2552 * feed=meatmeal,casein,sunflower +
49.3409 * feed=casein,sunflower +
160.2
Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
Class
Variable control
======================
spontaneous -1.1972
induced -0.4181
Intercept 1.7078
Odds Ratios...
Class
Variable control
======================
spontaneous 0.302
induced 0.6583
SMO
Kernel used:
RBF Kernel: K(x,y) = exp(-2.0*(x-y)^2)
Classifier for classes: setosa, versicolor
BinarySMO
- 1 * < 0.222222 0.541667 0.118644 0.166667> * X]
- 0.3223 * < 0.388889 1 0.084746 0.125> * X]
+ 1 * < 0.222222 0.208333 0.338983 0.416667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
+ 1 * < 0.166667 0.166667 0.389831 0.375> * X]
- 0.265 * < 0.194444 0.416667 0.101695 0.041667> * X]
+ 0.3856 * < 0.194444 0.125 0.389831 0.375> * X]
+ 0.5122 * < 0.75 0.5 0.627119 0.541667> * X]
- 0.525 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.2145 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 0.0262
Number of support vectors: 10
Number of kernel evaluations: 1842 (65.749% cached)
Classifier for classes: setosa, virginica
BinarySMO
1 * < 0.166667 0.208333 0.59322 0.666667> * X]
- 0.8549 * < 0.388889 1 0.084746 0.125> * X]
+ 0.651 * < 1 0.75 0.915254 0.791667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
- 0.1612 * < 0.222222 0.541667 0.118644 0.166667> * X]
+ 0.1957 * < 0.472222 0.083333 0.677966 0.583333> * X]
- 0.1255 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.295 * < 0.944444 0.25 1 0.916667> * X]
+ 0.0856
Number of support vectors: 8
Number of kernel evaluations: 2113 (69.684% cached)
Classifier for classes: versicolor, virginica
BinarySMO
1 * < 0.555556 0.208333 0.677966 0.75> * X]
- 1 * < 0.305556 0.416667 0.59322 0.583333> * X]
- 1 * < 0.666667 0.458333 0.627119 0.583333> * X]
- 1 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 1 * < 0.444444 0.416667 0.694915 0.708333> * X]
- 1 * < 0.527778 0.083333 0.59322 0.583333> * X]
+ 0.3452 * < 1 0.75 0.915254 0.791667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
- 1 * < 0.472222 0.291667 0.694915 0.625> * X]
+ 0.7325 * < 0.555556 0.375 0.779661 0.708333> * X]
- 1 * < 0.666667 0.416667 0.677966 0.666667> * X]
- 0.2861 * < 0.75 0.5 0.627119 0.541667> * X]
+ 1 * < 0.611111 0.416667 0.762712 0.708333> * X]
- 1 * < 0.5 0.375 0.627119 0.541667> * X]
- 1 * < 0.722222 0.458333 0.661017 0.583333> * X]
+ 1 * < 0.472222 0.083333 0.677966 0.583333> * X]
+ 1 * < 0.583333 0.458333 0.762712 0.708333> * X]
+ 1 * < 0.611111 0.5 0.694915 0.791667> * X]
+ 1 * < 0.5 0.416667 0.661017 0.708333> * X]
- 1 * < 0.694444 0.333333 0.644068 0.541667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
+ 1 * < 0.527778 0.333333 0.644068 0.708333> * X]
- 1 * < 0.444444 0.5 0.644068 0.708333> * X]
+ 1 * < 0.5 0.25 0.779661 0.541667> * X]
+ 0.5817 * < 0.805556 0.5 0.847458 0.708333> * X]
+ 1 * < 0.555556 0.291667 0.661017 0.708333> * X]
+ 0.1465 * < 0.361111 0.333333 0.661017 0.791667> * X]
- 1 * < 0.555556 0.208333 0.661017 0.583333> * X]
- 1 * < 0.555556 0.125 0.576271 0.5> * X]
+ 1 * < 0.555556 0.333333 0.694915 0.583333> * X]
+ 1 * < 0.166667 0.208333 0.59322 0.666667> * X]
+ 1 * < 0.805556 0.416667 0.813559 0.625> * X]
- 1 * < 0.555556 0.541667 0.627119 0.625> * X]
+ 1 * < 0.472222 0.416667 0.644068 0.708333> * X]
- 1 * < 0.361111 0.416667 0.59322 0.583333> * X]
- 0.7282 * < 0.583333 0.5 0.59322 0.583333> * X]
- 0.7915 * < 0.333333 0.125 0.508475 0.5> * X]
- 1 * < 0.472222 0.375 0.59322 0.583333> * X]
- 1 * < 0.611111 0.333333 0.610169 0.583333> * X]
+ 0.0916
Number of support vectors: 39
Number of kernel evaluations: 3726 (76.576% cached)
Linear Regression Model
weight =
73.4538 * feed=linseed,soybean,meatmeal,casein,sunflower +
43.2552 * feed=meatmeal,casein,sunflower +
49.3409 * feed=casein,sunflower +
160.2
Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
Class
Variable control
======================
spontaneous -1.1972
induced -0.4181
Intercept 1.7078
Odds Ratios...
Class
Variable control
======================
spontaneous 0.302
induced 0.6583
SMO
Kernel used:
RBF Kernel: K(x,y) = exp(-2.0*(x-y)^2)
Classifier for classes: setosa, versicolor
BinarySMO
- 1 * < 0.222222 0.541667 0.118644 0.166667> * X]
- 0.3223 * < 0.388889 1 0.084746 0.125> * X]
+ 1 * < 0.222222 0.208333 0.338983 0.416667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
+ 1 * < 0.166667 0.166667 0.389831 0.375> * X]
- 0.265 * < 0.194444 0.416667 0.101695 0.041667> * X]
+ 0.3856 * < 0.194444 0.125 0.389831 0.375> * X]
+ 0.5122 * < 0.75 0.5 0.627119 0.541667> * X]
- 0.525 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.2145 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 0.0262
Number of support vectors: 10
Number of kernel evaluations: 1842 (65.749% cached)
Classifier for classes: setosa, virginica
BinarySMO
1 * < 0.166667 0.208333 0.59322 0.666667> * X]
- 0.8549 * < 0.388889 1 0.084746 0.125> * X]
+ 0.651 * < 1 0.75 0.915254 0.791667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
- 0.1612 * < 0.222222 0.541667 0.118644 0.166667> * X]
+ 0.1957 * < 0.472222 0.083333 0.677966 0.583333> * X]
- 0.1255 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.295 * < 0.944444 0.25 1 0.916667> * X]
+ 0.0856
Number of support vectors: 8
Number of kernel evaluations: 2113 (69.684% cached)
Classifier for classes: versicolor, virginica
BinarySMO
1 * < 0.555556 0.208333 0.677966 0.75> * X]
- 1 * < 0.305556 0.416667 0.59322 0.583333> * X]
- 1 * < 0.666667 0.458333 0.627119 0.583333> * X]
- 1 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 1 * < 0.444444 0.416667 0.694915 0.708333> * X]
- 1 * < 0.527778 0.083333 0.59322 0.583333> * X]
+ 0.3452 * < 1 0.75 0.915254 0.791667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
- 1 * < 0.472222 0.291667 0.694915 0.625> * X]
+ 0.7325 * < 0.555556 0.375 0.779661 0.708333> * X]
- 1 * < 0.666667 0.416667 0.677966 0.666667> * X]
- 0.2861 * < 0.75 0.5 0.627119 0.541667> * X]
+ 1 * < 0.611111 0.416667 0.762712 0.708333> * X]
- 1 * < 0.5 0.375 0.627119 0.541667> * X]
- 1 * < 0.722222 0.458333 0.661017 0.583333> * X]
+ 1 * < 0.472222 0.083333 0.677966 0.583333> * X]
+ 1 * < 0.583333 0.458333 0.762712 0.708333> * X]
+ 1 * < 0.611111 0.5 0.694915 0.791667> * X]
+ 1 * < 0.5 0.416667 0.661017 0.708333> * X]
- 1 * < 0.694444 0.333333 0.644068 0.541667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
+ 1 * < 0.527778 0.333333 0.644068 0.708333> * X]
- 1 * < 0.444444 0.5 0.644068 0.708333> * X]
+ 1 * < 0.5 0.25 0.779661 0.541667> * X]
+ 0.5817 * < 0.805556 0.5 0.847458 0.708333> * X]
+ 1 * < 0.555556 0.291667 0.661017 0.708333> * X]
+ 0.1465 * < 0.361111 0.333333 0.661017 0.791667> * X]
- 1 * < 0.555556 0.208333 0.661017 0.583333> * X]
- 1 * < 0.555556 0.125 0.576271 0.5> * X]
+ 1 * < 0.555556 0.333333 0.694915 0.583333> * X]
+ 1 * < 0.166667 0.208333 0.59322 0.666667> * X]
+ 1 * < 0.805556 0.416667 0.813559 0.625> * X]
- 1 * < 0.555556 0.541667 0.627119 0.625> * X]
+ 1 * < 0.472222 0.416667 0.644068 0.708333> * X]
- 1 * < 0.361111 0.416667 0.59322 0.583333> * X]
- 0.7282 * < 0.583333 0.5 0.59322 0.583333> * X]
- 0.7915 * < 0.333333 0.125 0.508475 0.5> * X]
- 1 * < 0.472222 0.375 0.59322 0.583333> * X]
- 1 * < 0.611111 0.333333 0.610169 0.583333> * X]
+ 0.0916
Number of support vectors: 39
Number of kernel evaluations: 3726 (76.576% cached)
IB1 instance-based classifier
using 1 nearest neighbour(s) for classification
AdaBoostM1: Base classifiers and their weights:
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.5 0.5
Petal.Length is missing
setosa versicolor virginica
0.3333333333333333 0.3333333333333333 0.3333333333333333
Weight: 0.69
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.3333333333333333 0.6666666666666667
Petal.Length is missing
setosa versicolor virginica
0.25 0.25 0.5000000000000001
Weight: 1.1
Decision Stump
Classifications
Petal.Width <= 1.75 : versicolor
Petal.Width > 1.75 : virginica
Petal.Width is missing : versicolor
Class distributions
Petal.Width <= 1.75
setosa versicolor virginica
0.24154589371980675 0.7101449275362319 0.04830917874396136
Petal.Width > 1.75
setosa versicolor virginica
0.0 0.032258064516129024 0.967741935483871
Petal.Width is missing
setosa versicolor virginica
0.16666666666666666 0.5 0.33333333333333337
Weight: 1.32
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
2.4541772123292936E-17 0.5536309127248501 0.44636908727514996
Petal.Length is missing
setosa versicolor virginica
0.3968253968253968 0.33393610608800484 0.2692384970865984
Weight: 1.0
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
4.876852104094113E-17 0.31364408378939285 0.6863559162106071
Petal.Length is missing
setosa versicolor virginica
0.27151498487764564 0.22848501512235284 0.5000000000000016
Weight: 1.22
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-1.2574765214077044E-17 0.6067682992755968 0.3932317007244033
Petal.Length is missing
setosa versicolor virginica
0.17596222380612905 0.4999999999999999 0.32403777619387103
Weight: 0.74
Decision Stump
Classifications
Petal.Length <= 4.85 : versicolor
Petal.Length > 4.85 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 4.85
setosa versicolor virginica
0.2517002096709582 0.660989158028418 0.08731063230062384
Petal.Length > 4.85
setosa versicolor virginica
1.379474925594973E-17 0.058064332648205416 0.9419356673517946
Petal.Length is missing
setosa versicolor virginica
0.1301568472386974 0.3698431527613032 0.49999999999999944
Weight: 1.37
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-2.17703546955402E-18 0.4168896637524031 0.5831103362475969
Petal.Length is missing
setosa versicolor virginica
0.32003984920368916 0.28346835863050734 0.39649179216580344
Weight: 0.93
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
3.621292691813958E-17 0.6437704901626135 0.35622950983738655
Petal.Length is missing
setosa versicolor virginica
0.2233256919345557 0.4999999999999997 0.2766743080654446
Weight: 0.96
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
1.1378476401045689E-17 0.4087218990793083 0.5912781009206917
Petal.Length is missing
setosa versicolor virginica
0.15437422894330288 0.3456257710566977 0.4999999999999995
Weight: 0.64
Number of performed Iterations: 10
Bagging with 10 iterations and base learner
weka.classifiers.trees.REPTree -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0
LogitBoost: Base classifiers and their weights:
Iteration 1
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 3.0000000000000027
Petal.Length > 2.45 : -1.5
Petal.Length is missing : 1.9599137128049413E-15
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 0.8 : -1.5000000000000013
Petal.Width > 0.8 : 0.7500000000000003
Petal.Width is missing : -7.608728462097734E-16
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -1.2836538461538467
Petal.Width > 1.75 : 2.9021739130434803
Petal.Width is missing : 1.6283271027835617E-15
Iteration 2
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.107298632804185
Petal.Length > 2.45 : -1.1494279893253192
Petal.Length is missing : -0.3957206900169236
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 4.95 : 0.8927072824773201
Petal.Length > 4.95 : -1.281877805427337
Petal.Length is missing : 0.13310268365013825
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 4.85 : -1.0114659062621487
Petal.Length > 4.85 : 1.3395195188507139
Petal.Length is missing : 0.12532848791571008
Iteration 3
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0571563837220312
Petal.Width > 0.8 : -1.0537431091378222
Petal.Width is missing : -0.12564217216283943
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.65 : 0.35336467860151544
Petal.Width > 1.65 : -0.8272889203902701
Petal.Width is missing : 0.021646893923887404
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.65 : -0.5554268198088237
Petal.Width > 1.65 : 0.9085259163893444
Petal.Width is missing : 0.057019234645703
Iteration 4
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0317708097109874
Petal.Length > 2.45 : -1.025782418471695
Petal.Length is missing : 0.028704205441294997
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.2032301693639639
Petal.Length > 5.15 : -1.6167505374155677
Petal.Length is missing : -0.029665612140833936
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.3574866748606593
Petal.Length > 5.15 : 1.6858123253924724
Petal.Length is missing : 0.02407415847193774
Iteration 5
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0174288460889573
Petal.Width > 0.8 : -1.0121734540399154
Petal.Width is missing : 0.14037130816438537
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.8085139247966666
Sepal.Length > 4.95 : 0.17385428460727828
Sepal.Length is missing : -0.07058499672025116
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 2.8499999999999996 : 0.7604017682287972
Sepal.Width > 2.8499999999999996 : -0.9425445176141151
Sepal.Width is missing : 0.036618686875232266
Iteration 6
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0082467446058936
Petal.Length > 2.45 : -1.0070149682146894
Petal.Length is missing : 0.007908072736191237
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.45 : 0.5557671865473288
Petal.Width > 1.45 : -0.3122484934314317
Petal.Width is missing : -0.005865013040006491
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.35 : -1.0490610677454482
Petal.Width > 1.35 : 0.3286632987456759
Petal.Width is missing : 0.005223499537758138
Iteration 7
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0059118577482615
Petal.Width > 0.8 : -1.003405443224416
Petal.Width is missing : 0.23621249641952188
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 6.55 : -0.07598465327608493
Sepal.Length > 6.55 : 1.1183107264322862
Sepal.Length is missing : 0.12302823999845726
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 3.1500000000000004 : 0.04554114749362306
Sepal.Width > 3.1500000000000004 : -1.9197981583325392
Sepal.Width is missing : -0.16064487947532546
Iteration 8
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.002838400313738
Petal.Length > 2.45 : -1.0016483326432501
Petal.Length is missing : 0.1949327218316983
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.10428326648854233
Petal.Length > 5.15 : -1.1835612905032828
Petal.Length is missing : -0.06263074067522303
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.12453497884995612
Petal.Length > 5.15 : 1.184599934085556
Petal.Length is missing : 0.05459419319287985
Iteration 9
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.001558831849546
Petal.Width > 0.8 : -1.000847094753804
Petal.Width is missing : 0.27087980423892677
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.3433092354251668
Sepal.Length > 4.95 : 0.05737731913335332
Sepal.Length is missing : -0.07412404358407221
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Length <= 4.95 : 1.3690177365198544
Sepal.Length > 4.95 : -0.06609761928681071
Sepal.Length is missing : 0.06745461045228492
Iteration 10
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0008685918988107
Petal.Length > 2.45 : -1.0004573371926975
Petal.Length is missing : 0.25063512727922765
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.75 : 0.3702482917142169
Petal.Width > 1.75 : -0.7012067772823177
Petal.Width is missing : 0.018104326215643476
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -0.3841943142699475
Petal.Width > 1.75 : 0.7027054245666945
Petal.Width is missing : -0.02226933911584284
Number of performed iterations: 10
Stacking
Base classifiers
ZeroR predicts class value: setosa
Meta classifier
ZeroR predicts class value: setosa
AdaBoostM1: Base classifiers and their weights:
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.5 0.5
Petal.Length is missing
setosa versicolor virginica
0.3333333333333333 0.3333333333333333 0.3333333333333333
Weight: 0.69
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.3333333333333333 0.6666666666666667
Petal.Length is missing
setosa versicolor virginica
0.25 0.25 0.5000000000000001
Weight: 1.1
Decision Stump
Classifications
Petal.Width <= 1.75 : versicolor
Petal.Width > 1.75 : virginica
Petal.Width is missing : versicolor
Class distributions
Petal.Width <= 1.75
setosa versicolor virginica
0.24154589371980675 0.7101449275362319 0.04830917874396136
Petal.Width > 1.75
setosa versicolor virginica
0.0 0.032258064516129024 0.967741935483871
Petal.Width is missing
setosa versicolor virginica
0.16666666666666666 0.5 0.33333333333333337
Weight: 1.32
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
2.4541772123292936E-17 0.5536309127248501 0.44636908727514996
Petal.Length is missing
setosa versicolor virginica
0.3968253968253968 0.33393610608800484 0.2692384970865984
Weight: 1.0
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
4.876852104094113E-17 0.31364408378939285 0.6863559162106071
Petal.Length is missing
setosa versicolor virginica
0.27151498487764564 0.22848501512235284 0.5000000000000016
Weight: 1.22
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-1.2574765214077044E-17 0.6067682992755968 0.3932317007244033
Petal.Length is missing
setosa versicolor virginica
0.17596222380612905 0.4999999999999999 0.32403777619387103
Weight: 0.74
Decision Stump
Classifications
Petal.Length <= 4.85 : versicolor
Petal.Length > 4.85 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 4.85
setosa versicolor virginica
0.2517002096709582 0.660989158028418 0.08731063230062384
Petal.Length > 4.85
setosa versicolor virginica
1.379474925594973E-17 0.058064332648205416 0.9419356673517946
Petal.Length is missing
setosa versicolor virginica
0.1301568472386974 0.3698431527613032 0.49999999999999944
Weight: 1.37
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-2.17703546955402E-18 0.4168896637524031 0.5831103362475969
Petal.Length is missing
setosa versicolor virginica
0.32003984920368916 0.28346835863050734 0.39649179216580344
Weight: 0.93
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
3.621292691813958E-17 0.6437704901626135 0.35622950983738655
Petal.Length is missing
setosa versicolor virginica
0.2233256919345557 0.4999999999999997 0.2766743080654446
Weight: 0.96
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
1.1378476401045689E-17 0.4087218990793083 0.5912781009206917
Petal.Length is missing
setosa versicolor virginica
0.15437422894330288 0.3456257710566977 0.4999999999999995
Weight: 0.64
Number of performed Iterations: 10
Bagging with 10 iterations and base learner
weka.classifiers.trees.REPTree -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0
LogitBoost: Base classifiers and their weights:
Iteration 1
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 3.0000000000000027
Petal.Length > 2.45 : -1.5
Petal.Length is missing : 1.9599137128049413E-15
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 0.8 : -1.5000000000000013
Petal.Width > 0.8 : 0.7500000000000003
Petal.Width is missing : -7.608728462097734E-16
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -1.2836538461538467
Petal.Width > 1.75 : 2.9021739130434803
Petal.Width is missing : 1.6283271027835617E-15
Iteration 2
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.107298632804185
Petal.Length > 2.45 : -1.1494279893253192
Petal.Length is missing : -0.3957206900169236
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 4.95 : 0.8927072824773201
Petal.Length > 4.95 : -1.281877805427337
Petal.Length is missing : 0.13310268365013825
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 4.85 : -1.0114659062621487
Petal.Length > 4.85 : 1.3395195188507139
Petal.Length is missing : 0.12532848791571008
Iteration 3
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0571563837220312
Petal.Width > 0.8 : -1.0537431091378222
Petal.Width is missing : -0.12564217216283943
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.65 : 0.35336467860151544
Petal.Width > 1.65 : -0.8272889203902701
Petal.Width is missing : 0.021646893923887404
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.65 : -0.5554268198088237
Petal.Width > 1.65 : 0.9085259163893444
Petal.Width is missing : 0.057019234645703
Iteration 4
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0317708097109874
Petal.Length > 2.45 : -1.025782418471695
Petal.Length is missing : 0.028704205441294997
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.2032301693639639
Petal.Length > 5.15 : -1.6167505374155677
Petal.Length is missing : -0.029665612140833936
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.3574866748606593
Petal.Length > 5.15 : 1.6858123253924724
Petal.Length is missing : 0.02407415847193774
Iteration 5
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0174288460889573
Petal.Width > 0.8 : -1.0121734540399154
Petal.Width is missing : 0.14037130816438537
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.8085139247966666
Sepal.Length > 4.95 : 0.17385428460727828
Sepal.Length is missing : -0.07058499672025116
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 2.8499999999999996 : 0.7604017682287972
Sepal.Width > 2.8499999999999996 : -0.9425445176141151
Sepal.Width is missing : 0.036618686875232266
Iteration 6
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0082467446058936
Petal.Length > 2.45 : -1.0070149682146894
Petal.Length is missing : 0.007908072736191237
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.45 : 0.5557671865473288
Petal.Width > 1.45 : -0.3122484934314317
Petal.Width is missing : -0.005865013040006491
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.35 : -1.0490610677454482
Petal.Width > 1.35 : 0.3286632987456759
Petal.Width is missing : 0.005223499537758138
Iteration 7
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0059118577482615
Petal.Width > 0.8 : -1.003405443224416
Petal.Width is missing : 0.23621249641952188
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 6.55 : -0.07598465327608493
Sepal.Length > 6.55 : 1.1183107264322862
Sepal.Length is missing : 0.12302823999845726
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 3.1500000000000004 : 0.04554114749362306
Sepal.Width > 3.1500000000000004 : -1.9197981583325392
Sepal.Width is missing : -0.16064487947532546
Iteration 8
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.002838400313738
Petal.Length > 2.45 : -1.0016483326432501
Petal.Length is missing : 0.1949327218316983
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.10428326648854233
Petal.Length > 5.15 : -1.1835612905032828
Petal.Length is missing : -0.06263074067522303
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.12453497884995612
Petal.Length > 5.15 : 1.184599934085556
Petal.Length is missing : 0.05459419319287985
Iteration 9
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.001558831849546
Petal.Width > 0.8 : -1.000847094753804
Petal.Width is missing : 0.27087980423892677
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.3433092354251668
Sepal.Length > 4.95 : 0.05737731913335332
Sepal.Length is missing : -0.07412404358407221
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Length <= 4.95 : 1.3690177365198544
Sepal.Length > 4.95 : -0.06609761928681071
Sepal.Length is missing : 0.06745461045228492
Iteration 10
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0008685918988107
Petal.Length > 2.45 : -1.0004573371926975
Petal.Length is missing : 0.25063512727922765
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.75 : 0.3702482917142169
Petal.Width > 1.75 : -0.7012067772823177
Petal.Width is missing : 0.018104326215643476
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -0.3841943142699475
Petal.Width > 1.75 : 0.7027054245666945
Petal.Width is missing : -0.02226933911584284
Number of performed iterations: 10
Stacking
Base classifiers
ZeroR predicts class value: setosa
Meta classifier
ZeroR predicts class value: setosa
AdaBoostM1: Base classifiers and their weights:
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.5 0.5
Petal.Length is missing
setosa versicolor virginica
0.3333333333333333 0.3333333333333333 0.3333333333333333
Weight: 0.69
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.3333333333333333 0.6666666666666667
Petal.Length is missing
setosa versicolor virginica
0.25 0.25 0.5000000000000001
Weight: 1.1
Decision Stump
Classifications
Petal.Width <= 1.75 : versicolor
Petal.Width > 1.75 : virginica
Petal.Width is missing : versicolor
Class distributions
Petal.Width <= 1.75
setosa versicolor virginica
0.24154589371980675 0.7101449275362319 0.04830917874396136
Petal.Width > 1.75
setosa versicolor virginica
0.0 0.032258064516129024 0.967741935483871
Petal.Width is missing
setosa versicolor virginica
0.16666666666666666 0.5 0.33333333333333337
Weight: 1.32
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
2.4541772123292936E-17 0.5536309127248501 0.44636908727514996
Petal.Length is missing
setosa versicolor virginica
0.3968253968253968 0.33393610608800484 0.2692384970865984
Weight: 1.0
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
4.876852104094113E-17 0.31364408378939285 0.6863559162106071
Petal.Length is missing
setosa versicolor virginica
0.27151498487764564 0.22848501512235284 0.5000000000000016
Weight: 1.22
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-1.2574765214077044E-17 0.6067682992755968 0.3932317007244033
Petal.Length is missing
setosa versicolor virginica
0.17596222380612905 0.4999999999999999 0.32403777619387103
Weight: 0.74
Decision Stump
Classifications
Petal.Length <= 4.85 : versicolor
Petal.Length > 4.85 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 4.85
setosa versicolor virginica
0.2517002096709582 0.660989158028418 0.08731063230062384
Petal.Length > 4.85
setosa versicolor virginica
1.379474925594973E-17 0.058064332648205416 0.9419356673517946
Petal.Length is missing
setosa versicolor virginica
0.1301568472386974 0.3698431527613032 0.49999999999999944
Weight: 1.37
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-2.17703546955402E-18 0.4168896637524031 0.5831103362475969
Petal.Length is missing
setosa versicolor virginica
0.32003984920368916 0.28346835863050734 0.39649179216580344
Weight: 0.93
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
3.621292691813958E-17 0.6437704901626135 0.35622950983738655
Petal.Length is missing
setosa versicolor virginica
0.2233256919345557 0.4999999999999997 0.2766743080654446
Weight: 0.96
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
1.1378476401045689E-17 0.4087218990793083 0.5912781009206917
Petal.Length is missing
setosa versicolor virginica
0.15437422894330288 0.3456257710566977 0.4999999999999995
Weight: 0.64
Number of performed Iterations: 10
Bagging with 10 iterations and base learner
weka.classifiers.trees.REPTree -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0
LogitBoost: Base classifiers and their weights:
Iteration 1
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 3.0000000000000027
Petal.Length > 2.45 : -1.5
Petal.Length is missing : 1.9599137128049413E-15
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 0.8 : -1.5000000000000013
Petal.Width > 0.8 : 0.7500000000000003
Petal.Width is missing : -7.608728462097734E-16
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -1.2836538461538467
Petal.Width > 1.75 : 2.9021739130434803
Petal.Width is missing : 1.6283271027835617E-15
Iteration 2
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.107298632804185
Petal.Length > 2.45 : -1.1494279893253192
Petal.Length is missing : -0.3957206900169236
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 4.95 : 0.8927072824773201
Petal.Length > 4.95 : -1.281877805427337
Petal.Length is missing : 0.13310268365013825
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 4.85 : -1.0114659062621487
Petal.Length > 4.85 : 1.3395195188507139
Petal.Length is missing : 0.12532848791571008
Iteration 3
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0571563837220312
Petal.Width > 0.8 : -1.0537431091378222
Petal.Width is missing : -0.12564217216283943
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.65 : 0.35336467860151544
Petal.Width > 1.65 : -0.8272889203902701
Petal.Width is missing : 0.021646893923887404
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.65 : -0.5554268198088237
Petal.Width > 1.65 : 0.9085259163893444
Petal.Width is missing : 0.057019234645703
Iteration 4
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0317708097109874
Petal.Length > 2.45 : -1.025782418471695
Petal.Length is missing : 0.028704205441294997
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.2032301693639639
Petal.Length > 5.15 : -1.6167505374155677
Petal.Length is missing : -0.029665612140833936
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.3574866748606593
Petal.Length > 5.15 : 1.6858123253924724
Petal.Length is missing : 0.02407415847193774
Iteration 5
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0174288460889573
Petal.Width > 0.8 : -1.0121734540399154
Petal.Width is missing : 0.14037130816438537
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.8085139247966666
Sepal.Length > 4.95 : 0.17385428460727828
Sepal.Length is missing : -0.07058499672025116
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 2.8499999999999996 : 0.7604017682287972
Sepal.Width > 2.8499999999999996 : -0.9425445176141151
Sepal.Width is missing : 0.036618686875232266
Iteration 6
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0082467446058936
Petal.Length > 2.45 : -1.0070149682146894
Petal.Length is missing : 0.007908072736191237
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.45 : 0.5557671865473288
Petal.Width > 1.45 : -0.3122484934314317
Petal.Width is missing : -0.005865013040006491
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.35 : -1.0490610677454482
Petal.Width > 1.35 : 0.3286632987456759
Petal.Width is missing : 0.005223499537758138
Iteration 7
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0059118577482615
Petal.Width > 0.8 : -1.003405443224416
Petal.Width is missing : 0.23621249641952188
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 6.55 : -0.07598465327608493
Sepal.Length > 6.55 : 1.1183107264322862
Sepal.Length is missing : 0.12302823999845726
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 3.1500000000000004 : 0.04554114749362306
Sepal.Width > 3.1500000000000004 : -1.9197981583325392
Sepal.Width is missing : -0.16064487947532546
Iteration 8
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.002838400313738
Petal.Length > 2.45 : -1.0016483326432501
Petal.Length is missing : 0.1949327218316983
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.10428326648854233
Petal.Length > 5.15 : -1.1835612905032828
Petal.Length is missing : -0.06263074067522303
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.12453497884995612
Petal.Length > 5.15 : 1.184599934085556
Petal.Length is missing : 0.05459419319287985
Iteration 9
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.001558831849546
Petal.Width > 0.8 : -1.000847094753804
Petal.Width is missing : 0.27087980423892677
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.3433092354251668
Sepal.Length > 4.95 : 0.05737731913335332
Sepal.Length is missing : -0.07412404358407221
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Length <= 4.95 : 1.3690177365198544
Sepal.Length > 4.95 : -0.06609761928681071
Sepal.Length is missing : 0.06745461045228492
Iteration 10
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0008685918988107
Petal.Length > 2.45 : -1.0004573371926975
Petal.Length is missing : 0.25063512727922765
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.75 : 0.3702482917142169
Petal.Width > 1.75 : -0.7012067772823177
Petal.Width is missing : 0.018104326215643476
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -0.3841943142699475
Petal.Width > 1.75 : 0.7027054245666945
Petal.Width is missing : -0.02226933911584284
Number of performed iterations: 10
Stacking
Base classifiers
ZeroR predicts class value: setosa
Meta classifier
ZeroR predicts class value: setosa
JRIP rules:
===========
(Petal.Length <= 1.9) => Species=setosa (50.0/0.0)
(Petal.Width <= 1.7) and (Petal.Length <= 4.9) => Species=versicolor (48.0/1.0)
=> Species=virginica (52.0/3.0)
Number of Rules : 3
M5 pruned model rules
(using smoothed linear models) :
Number of Rules : 2
Rule: 1
IF
cyl > 5
THEN
mpg =
-0.5389 * cyl
+ 0.0048 * disp
- 0.0206 * hp
- 3.0997 * wt
+ 34.4212 [21/26.733%]
Rule: 2
mpg =
-0.1351 * disp
+ 40.872 [11/59.295%]
Petal.Width:
< 0.8 -> setosa
< 1.75 -> versicolor
>= 1.75 -> virginica
(144/150 instances correct)
PART decision list
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width <= 1.7 AND
Petal.Length <= 4.9: versicolor (48.0/1.0)
: virginica (52.0/3.0)
Number of Rules : 3
JRIP rules:
===========
(Petal.Length <= 1.9) => Species=setosa (50.0/0.0)
(Petal.Width <= 1.7) and (Petal.Length <= 4.9) => Species=versicolor (48.0/1.0)
=> Species=virginica (52.0/3.0)
Number of Rules : 3
M5 pruned model rules
(using smoothed linear models) :
Number of Rules : 2
Rule: 1
IF
cyl > 5
THEN
mpg =
-0.5389 * cyl
+ 0.0048 * disp
- 0.0206 * hp
- 3.0997 * wt
+ 34.4212 [21/26.733%]
Rule: 2
mpg =
-0.1351 * disp
+ 40.872 [11/59.295%]
Petal.Width:
< 0.8 -> setosa
< 1.75 -> versicolor
>= 1.75 -> virginica
(144/150 instances correct)
PART decision list
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width <= 1.7 AND
Petal.Length <= 4.9: versicolor (48.0/1.0)
: virginica (52.0/3.0)
Number of Rules : 3
JRIP rules:
===========
(Petal.Length <= 1.9) => Species=setosa (50.0/0.0)
(Petal.Width <= 1.7) and (Petal.Length <= 4.9) => Species=versicolor (48.0/1.0)
=> Species=virginica (52.0/3.0)
Number of Rules : 3
M5 pruned model rules
(using smoothed linear models) :
Number of Rules : 2
Rule: 1
IF
cyl > 5
THEN
mpg =
-0.5389 * cyl
+ 0.0048 * disp
- 0.0206 * hp
- 3.0997 * wt
+ 34.4212 [21/26.733%]
Rule: 2
mpg =
-0.1351 * disp
+ 40.872 [11/59.295%]
Petal.Width:
< 0.8 -> setosa
< 1.75 -> versicolor
>= 1.75 -> virginica
(144/150 instances correct)
PART decision list
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width <= 1.7 AND
Petal.Length <= 4.9: versicolor (48.0/1.0)
: virginica (52.0/3.0)
Number of Rules : 3
Decision Stump
Classifications
outlook = overcast : yes
outlook != overcast : yes
outlook is missing : yes
Class distributions
outlook = overcast
yes no
1.0 0.0
outlook != overcast
yes no
0.5 0.5
outlook is missing
yes no
0.6428571428571429 0.35714285714285715
J48 pruned tree
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
| Petal.Width <= 1.7
| | Petal.Length <= 4.9: versicolor (48.0/1.0)
| | Petal.Length > 4.9
| | | Petal.Width <= 1.5: virginica (3.0)
| | | Petal.Width > 1.5: versicolor (3.0/1.0)
| Petal.Width > 1.7: virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Logistic model tree
------------------
: LM_1:11/11 (14)
Number of Leaves : 1
Size of the Tree : 1
LM_1:
Class yes :
6.95 +
[outlook=sunny] * -0.65 +
[outlook=overcast] * 2.82 +
[temperature] * -0.02 +
[humidity] * -0.06 +
[windy=TRUE] * -1.38
Class no :
-6.95 +
[outlook=sunny] * 0.65 +
[outlook=overcast] * -2.82 +
[temperature] * 0.02 +
[humidity] * 0.06 +
[windy=TRUE] * 1.38
M5 pruned model tree:
(using smoothed linear models)
CHMIN <= 7.5 :
| MMAX <= 6100 : LM1 (76/7.046%)
| MMAX > 6100 :
| | CACH <= 28 :
| | | CACH <= 0.5 : LM2 (20/7.795%)
| | | CACH > 0.5 :
| | | | CHMIN <= 3.5 : LM3 (34/11.658%)
| | | | CHMIN > 3.5 : LM4 (9/12.866%)
| | CACH > 28 : LM5 (26/19.269%)
CHMIN > 7.5 :
| MMAX <= 28000 :
| | MMAX <= 13240 :
| | | CACH <= 81.5 : LM6 (6/18.551%)
| | | CACH > 81.5 : LM7 (4/30.824%)
| | MMAX > 13240 : LM8 (11/24.185%)
| MMAX > 28000 : LM9 (23/48.302%)
LM num: 1
class =
0.0025 * MMIN
+ 0.004 * MMAX
+ 0.0009 * CACH
+ 0.1758 * CHMIN
+ 0.205 * CHMAX
+ 7.6605
LM num: 2
class =
-0.012 * MYCT
- 0.0141 * MMIN
+ 0.0013 * MMAX
+ 0.0017 * CACH
+ 0.2793 * CHMIN
+ 0.6771 * CHMAX
+ 26.4438
LM num: 3
class =
-0.0096 * MMIN
+ 0.0028 * MMAX
+ 0.0018 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 24.6136
LM num: 4
class =
-0.0406 * MYCT
- 0.0096 * MMIN
+ 0.0033 * MMAX
- 0.294 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 38.0329
LM num: 5
class =
-0.0077 * MMIN
+ 0.0024 * MMAX
+ 0.3125 * CACH
+ 0.3866 * CHMIN
+ 0.5207 * CHMAX
+ 17.2559
LM num: 6
class =
-0.5146 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.3906 * CACH
- 2.0512 * CHMIN
+ 70.8672
LM num: 7
class =
-0.5896 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.4034 * CACH
- 2.0512 * CHMIN
+ 83.0016
LM num: 8
class =
-0.3653 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.2587 * CACH
- 1.957 * CHMIN
+ 82.5725
LM num: 9
class =
-0.4748 * MMIN
+ 0.0083 * MMAX
+ 0.003 * CACH
- 0.9387 * CHMIN
+ 2.2489 * CHMAX
- 51.8474
Number of Rules : 9
Decision Stump
Classifications
outlook = overcast : yes
outlook != overcast : yes
outlook is missing : yes
Class distributions
outlook = overcast
yes no
1.0 0.0
outlook != overcast
yes no
0.5 0.5
outlook is missing
yes no
0.6428571428571429 0.35714285714285715
J48 pruned tree
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
| Petal.Width <= 1.7
| | Petal.Length <= 4.9: versicolor (48.0/1.0)
| | Petal.Length > 4.9
| | | Petal.Width <= 1.5: virginica (3.0)
| | | Petal.Width > 1.5: versicolor (3.0/1.0)
| Petal.Width > 1.7: virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Logistic model tree
------------------
: LM_1:11/11 (14)
Number of Leaves : 1
Size of the Tree : 1
LM_1:
Class yes :
6.95 +
[outlook=sunny] * -0.65 +
[outlook=overcast] * 2.82 +
[temperature] * -0.02 +
[humidity] * -0.06 +
[windy=TRUE] * -1.38
Class no :
-6.95 +
[outlook=sunny] * 0.65 +
[outlook=overcast] * -2.82 +
[temperature] * 0.02 +
[humidity] * 0.06 +
[windy=TRUE] * 1.38
M5 pruned model tree:
(using smoothed linear models)
CHMIN <= 7.5 :
| MMAX <= 6100 : LM1 (76/7.046%)
| MMAX > 6100 :
| | CACH <= 28 :
| | | CACH <= 0.5 : LM2 (20/7.795%)
| | | CACH > 0.5 :
| | | | CHMIN <= 3.5 : LM3 (34/11.658%)
| | | | CHMIN > 3.5 : LM4 (9/12.866%)
| | CACH > 28 : LM5 (26/19.269%)
CHMIN > 7.5 :
| MMAX <= 28000 :
| | MMAX <= 13240 :
| | | CACH <= 81.5 : LM6 (6/18.551%)
| | | CACH > 81.5 : LM7 (4/30.824%)
| | MMAX > 13240 : LM8 (11/24.185%)
| MMAX > 28000 : LM9 (23/48.302%)
LM num: 1
class =
0.0025 * MMIN
+ 0.004 * MMAX
+ 0.0009 * CACH
+ 0.1758 * CHMIN
+ 0.205 * CHMAX
+ 7.6605
LM num: 2
class =
-0.012 * MYCT
- 0.0141 * MMIN
+ 0.0013 * MMAX
+ 0.0017 * CACH
+ 0.2793 * CHMIN
+ 0.6771 * CHMAX
+ 26.4438
LM num: 3
class =
-0.0096 * MMIN
+ 0.0028 * MMAX
+ 0.0018 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 24.6136
LM num: 4
class =
-0.0406 * MYCT
- 0.0096 * MMIN
+ 0.0033 * MMAX
- 0.294 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 38.0329
LM num: 5
class =
-0.0077 * MMIN
+ 0.0024 * MMAX
+ 0.3125 * CACH
+ 0.3866 * CHMIN
+ 0.5207 * CHMAX
+ 17.2559
LM num: 6
class =
-0.5146 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.3906 * CACH
- 2.0512 * CHMIN
+ 70.8672
LM num: 7
class =
-0.5896 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.4034 * CACH
- 2.0512 * CHMIN
+ 83.0016
LM num: 8
class =
-0.3653 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.2587 * CACH
- 1.957 * CHMIN
+ 82.5725
LM num: 9
class =
-0.4748 * MMIN
+ 0.0083 * MMAX
+ 0.003 * CACH
- 0.9387 * CHMIN
+ 2.2489 * CHMAX
- 51.8474
Number of Rules : 9
Decision Stump
Classifications
outlook = overcast : yes
outlook != overcast : yes
outlook is missing : yes
Class distributions
outlook = overcast
yes no
1.0 0.0
outlook != overcast
yes no
0.5 0.5
outlook is missing
yes no
0.6428571428571429 0.35714285714285715
J48 pruned tree
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
| Petal.Width <= 1.7
| | Petal.Length <= 4.9: versicolor (48.0/1.0)
| | Petal.Length > 4.9
| | | Petal.Width <= 1.5: virginica (3.0)
| | | Petal.Width > 1.5: versicolor (3.0/1.0)
| Petal.Width > 1.7: virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Logistic model tree
------------------
: LM_1:11/11 (14)
Number of Leaves : 1
Size of the Tree : 1
LM_1:
Class yes :
6.95 +
[outlook=sunny] * -0.65 +
[outlook=overcast] * 2.82 +
[temperature] * -0.02 +
[humidity] * -0.06 +
[windy=TRUE] * -1.38
Class no :
-6.95 +
[outlook=sunny] * 0.65 +
[outlook=overcast] * -2.82 +
[temperature] * 0.02 +
[humidity] * 0.06 +
[windy=TRUE] * 1.38
M5 pruned model tree:
(using smoothed linear models)
CHMIN <= 7.5 :
| MMAX <= 6100 : LM1 (76/7.046%)
| MMAX > 6100 :
| | CACH <= 28 :
| | | CACH <= 0.5 : LM2 (20/7.795%)
| | | CACH > 0.5 :
| | | | CHMIN <= 3.5 : LM3 (34/11.658%)
| | | | CHMIN > 3.5 : LM4 (9/12.866%)
| | CACH > 28 : LM5 (26/19.269%)
CHMIN > 7.5 :
| MMAX <= 28000 :
| | MMAX <= 13240 :
| | | CACH <= 81.5 : LM6 (6/18.551%)
| | | CACH > 81.5 : LM7 (4/30.824%)
| | MMAX > 13240 : LM8 (11/24.185%)
| MMAX > 28000 : LM9 (23/48.302%)
LM num: 1
class =
0.0025 * MMIN
+ 0.004 * MMAX
+ 0.0009 * CACH
+ 0.1758 * CHMIN
+ 0.205 * CHMAX
+ 7.6605
LM num: 2
class =
-0.012 * MYCT
- 0.0141 * MMIN
+ 0.0013 * MMAX
+ 0.0017 * CACH
+ 0.2793 * CHMIN
+ 0.6771 * CHMAX
+ 26.4438
LM num: 3
class =
-0.0096 * MMIN
+ 0.0028 * MMAX
+ 0.0018 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 24.6136
LM num: 4
class =
-0.0406 * MYCT
- 0.0096 * MMIN
+ 0.0033 * MMAX
- 0.294 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 38.0329
LM num: 5
class =
-0.0077 * MMIN
+ 0.0024 * MMAX
+ 0.3125 * CACH
+ 0.3866 * CHMIN
+ 0.5207 * CHMAX
+ 17.2559
LM num: 6
class =
-0.5146 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.3906 * CACH
- 2.0512 * CHMIN
+ 70.8672
LM num: 7
class =
-0.5896 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.4034 * CACH
- 2.0512 * CHMIN
+ 83.0016
LM num: 8
class =
-0.3653 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.2587 * CACH
- 1.957 * CHMIN
+ 82.5725
LM num: 9
class =
-0.4748 * MMIN
+ 0.0083 * MMAX
+ 0.003 * CACH
- 0.9387 * CHMIN
+ 2.2489 * CHMAX
- 51.8474
Number of Rules : 9
outlook temperature humidity windy play
1 sunny 'All' 'All' FALSE no
2 sunny 'All' 'All' TRUE no
3 overcast 'All' 'All' FALSE yes
4 rainy 'All' 'All' FALSE yes
5 rainy 'All' 'All' FALSE yes
6 rainy 'All' 'All' TRUE no
7 overcast 'All' 'All' TRUE yes
8 sunny 'All' 'All' FALSE no
9 sunny 'All' 'All' FALSE yes
10 rainy 'All' 'All' FALSE yes
11 sunny 'All' 'All' TRUE yes
12 overcast 'All' 'All' TRUE yes
13 overcast 'All' 'All' FALSE yes
14 rainy 'All' 'All' TRUE no
outlook temperature humidity windy play
1 sunny 1.00000000 0.6451613 FALSE no
2 sunny 0.76190476 0.8064516 TRUE no
3 overcast 0.90476190 0.6774194 FALSE yes
4 rainy 0.28571429 1.0000000 FALSE yes
5 rainy 0.19047619 0.4838710 FALSE yes
6 rainy 0.04761905 0.1612903 TRUE no
7 overcast 0.00000000 0.0000000 TRUE yes
8 sunny 0.38095238 0.9677419 FALSE no
9 sunny 0.23809524 0.1612903 FALSE yes
10 rainy 0.52380952 0.4838710 FALSE yes
11 sunny 0.52380952 0.1612903 TRUE yes
12 overcast 0.38095238 0.8064516 TRUE yes
13 overcast 0.80952381 0.3225806 FALSE yes
14 rainy 0.33333333 0.8387097 TRUE no
outlook temperature humidity windy play
1 sunny 'All' 'All' FALSE no
2 sunny 'All' 'All' TRUE no
3 overcast 'All' 'All' FALSE yes
4 rainy 'All' 'All' FALSE yes
5 rainy 'All' 'All' FALSE yes
6 rainy 'All' 'All' TRUE no
7 overcast 'All' 'All' TRUE yes
8 sunny 'All' 'All' FALSE no
9 sunny 'All' 'All' FALSE yes
10 rainy 'All' 'All' FALSE yes
11 sunny 'All' 'All' TRUE yes
12 overcast 'All' 'All' TRUE yes
13 overcast 'All' 'All' FALSE yes
14 rainy 'All' 'All' TRUE no
outlook temperature humidity windy play
1 sunny 1.00000000 0.6451613 FALSE no
2 sunny 0.76190476 0.8064516 TRUE no
3 overcast 0.90476190 0.6774194 FALSE yes
4 rainy 0.28571429 1.0000000 FALSE yes
5 rainy 0.19047619 0.4838710 FALSE yes
6 rainy 0.04761905 0.1612903 TRUE no
7 overcast 0.00000000 0.0000000 TRUE yes
8 sunny 0.38095238 0.9677419 FALSE no
9 sunny 0.23809524 0.1612903 FALSE yes
10 rainy 0.52380952 0.4838710 FALSE yes
11 sunny 0.52380952 0.1612903 TRUE yes
12 overcast 0.38095238 0.8064516 TRUE yes
13 overcast 0.80952381 0.3225806 FALSE yes
14 rainy 0.33333333 0.8387097 TRUE no
outlook temperature humidity windy play
1 sunny 'All' 'All' FALSE no
2 sunny 'All' 'All' TRUE no
3 overcast 'All' 'All' FALSE yes
4 rainy 'All' 'All' FALSE yes
5 rainy 'All' 'All' FALSE yes
6 rainy 'All' 'All' TRUE no
7 overcast 'All' 'All' TRUE yes
8 sunny 'All' 'All' FALSE no
9 sunny 'All' 'All' FALSE yes
10 rainy 'All' 'All' FALSE yes
11 sunny 'All' 'All' TRUE yes
12 overcast 'All' 'All' TRUE yes
13 overcast 'All' 'All' FALSE yes
14 rainy 'All' 'All' TRUE no
outlook temperature humidity windy play
1 sunny 1.00000000 0.6451613 FALSE no
2 sunny 0.76190476 0.8064516 TRUE no
3 overcast 0.90476190 0.6774194 FALSE yes
4 rainy 0.28571429 1.0000000 FALSE yes
5 rainy 0.19047619 0.4838710 FALSE yes
6 rainy 0.04761905 0.1612903 TRUE no
7 overcast 0.00000000 0.0000000 TRUE yes
8 sunny 0.38095238 0.9677419 FALSE no
9 sunny 0.23809524 0.1612903 FALSE yes
10 rainy 0.52380952 0.4838710 FALSE yes
11 sunny 0.52380952 0.1612903 TRUE yes
12 overcast 0.38095238 0.8064516 TRUE yes
13 overcast 0.80952381 0.3225806 FALSE yes
14 rainy 0.33333333 0.8387097 TRUE no
intubate documentation built on May 2, 2019, 2:46 p.m.
R Package Documentation
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Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to RWeka functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ntbt_AdaBoostM1(data, ...)
ntbt_Bagging(data, ...)
ntbt_CostSensitiveClassifier(data, ...)
ntbt_DecisionStump(data, ...)
ntbt_Discretize(data, ...)
ntbt_GainRatioAttributeEval(data, ...)
ntbt_IBk(data, ...)
ntbt_InfoGainAttributeEval(data, ...)
ntbt_J48(data, ...)
ntbt_JRip(data, ...)
ntbt_LBR(data, ...)
ntbt_LogitBoost(data, ...)
ntbt_LinearRegression(data, ...)
ntbt_LMT(data, ...)
ntbt_Logistic(data, ...)
ntbt_M5P(data, ...)
ntbt_M5Rules(data, ...)
ntbt_MultiBoostAB(data, ...)
ntbt_Normalize(data, ...)
ntbt_OneR(data, ...)
ntbt_PART(data, ...)
ntbt_SMO(data, ...)
ntbt_Stacking(data, ...)
|
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | ## Not run:
library(intubate)
library(magrittr)
library(RWeka)
## R/Weka Attribute Evaluators
## Original function to interface
GainRatioAttributeEval(Species ~ . , data = iris)
InfoGainAttributeEval(Species ~ . , data = iris)
## The interface puts data as first parameter
ntbt_GainRatioAttributeEval(iris, Species ~ .)
ntbt_InfoGainAttributeEval(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_GainRatioAttributeEval(Species ~ .)
iris %>%
ntbt_InfoGainAttributeEval(Species ~ .)
## R/Weka Classifier Functions
data(infert)
infert$STATUS <- factor(infert$case, labels = c("control", "case"))
## Original function to interface
LinearRegression(weight ~ feed, data = chickwts)
Logistic(STATUS ~ spontaneous + induced, data = infert)
SMO(Species ~ ., data = iris, control = Weka_control(K = list("RBFKernel", G = 2)))
## The interface puts data as first parameter
ntbt_LinearRegression(chickwts, weight ~ feed)
ntbt_Logistic(infert, STATUS ~ spontaneous + induced)
ntbt_SMO(iris, Species ~ ., control = Weka_control(K = list("RBFKernel", G = 2)))
## so it can be used easily in a pipeline.
chickwts %>%
ntbt_LinearRegression(weight ~ feed)
infert %>%
ntbt_Logistic(STATUS ~ spontaneous + induced)
iris %>%
ntbt_SMO(Species ~ ., control = Weka_control(K = list("RBFKernel", G = 2)))
## R/Weka Lazy Learners
## No examples provided. LBR seems to need 'lazyBayesianRules'
## and I am too lazy myself to install it
ntbt_IBk(chickwts, weight ~ feed) ## Example may not make sense
## R/Weka Meta Learners
## MultiBoostAB needs Weka package 'multiBoostAB'
## CostSensitiveClassifier throws an error
## Original function to interface
AdaBoostM1(Species ~ ., data = iris, control = Weka_control(W = "DecisionStump"))
Bagging(Species ~ ., data = iris, control = Weka_control())
LogitBoost(Species ~ ., data = iris, control = Weka_control())
Stacking(Species ~ ., data = iris, control = Weka_control())
## The interface puts data as first parameter
ntbt_AdaBoostM1(iris, Species ~ ., control = Weka_control(W = "DecisionStump"))
ntbt_Bagging(iris, Species ~ ., control = Weka_control())
ntbt_LogitBoost(iris, Species ~ ., control = Weka_control())
ntbt_Stacking(iris, Species ~ ., control = Weka_control())
## so it can be used easily in a pipeline.
iris %>%
ntbt_AdaBoostM1(Species ~ ., control = Weka_control(W = "DecisionStump"))
iris %>%
ntbt_Bagging(Species ~ ., control = Weka_control())
iris %>%
ntbt_LogitBoost(Species ~ ., control = Weka_control())
iris %>%
ntbt_Stacking(Species ~ ., control = Weka_control())
## R/Weka Rule Learners
## Original function to interface
JRip(Species ~ ., data = iris)
M5Rules(mpg ~ ., data = mtcars)
OneR(Species ~ ., data = iris)
PART(Species ~ ., data = iris)
## The interface puts data as first parameter
ntbt_JRip(iris, Species ~ .)
ntbt_M5Rules(mtcars, mpg ~ .)
ntbt_OneR(iris, Species ~ .)
ntbt_PART(iris, Species ~ .)
## so it can be used easily in a pipeline.
iris %>%
ntbt_JRip(Species ~ .)
mtcars %>%
ntbt_M5Rules(mpg ~ .)
iris %>%
ntbt_OneR(Species ~ .)
iris %>%
ntbt_PART(Species ~ .)
## R/Weka Classifier Trees
DF3 <- read.arff(system.file("arff", "cpu.arff", package = "RWeka"))
DF4 <- read.arff(system.file("arff", "weather.arff", package = "RWeka"))
## Original function to interface
DecisionStump(play ~ ., data = DF4)
J48(Species ~ ., data = iris)
LMT(play ~ ., data = DF4)
M5P(class ~ ., data = DF3)
## The interface puts data as first parameter
ntbt_DecisionStump(DF4, play ~ .)
ntbt_J48(iris, Species ~ .)
ntbt_LMT(DF4, play ~ .)
ntbt_M5P(DF3, class ~ .)
## so it can be used easily in a pipeline.
DF4 %>%
ntbt_DecisionStump(play ~ .)
iris %>%
ntbt_J48(Species ~ .)
DF4 %>%
ntbt_LMT(play ~ .)
DF3 %>%
ntbt_M5P(class ~ .)
## R/Weka Filters
w <- read.arff(system.file("arff","weather.arff", package = "RWeka"))
## Original function to interface
Discretize(play ~., data = w)
Normalize(~., data = w)
## The interface puts data as first parameter
ntbt_Discretize(w, play ~.)
ntbt_Normalize(w, ~.)
## so it can be used easily in a pipeline.
w %>%
ntbt_Discretize(play ~.)
w %>%
ntbt_Normalize(~.)
## End(Not run)
|
Example output
OpenJDK 64-Bit Server VM warning: Can't detect primordial thread stack location - find_vma failed
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.3811740 0.2472972 0.7339960 0.8713692
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.6982615 0.3855963 1.4180030 1.3784027
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.3811740 0.2472972 0.7339960 0.8713692
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.6982615 0.3855963 1.4180030 1.3784027
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.3811740 0.2472972 0.7339960 0.8713692
Sepal.Length Sepal.Width Petal.Length Petal.Width
0.6982615 0.3855963 1.4180030 1.3784027
Linear Regression Model
weight =
73.4538 * feed=linseed,soybean,meatmeal,casein,sunflower +
43.2552 * feed=meatmeal,casein,sunflower +
49.3409 * feed=casein,sunflower +
160.2
Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
Class
Variable control
======================
spontaneous -1.1972
induced -0.4181
Intercept 1.7078
Odds Ratios...
Class
Variable control
======================
spontaneous 0.302
induced 0.6583
SMO
Kernel used:
RBF Kernel: K(x,y) = exp(-2.0*(x-y)^2)
Classifier for classes: setosa, versicolor
BinarySMO
- 1 * < 0.222222 0.541667 0.118644 0.166667> * X]
- 0.3223 * < 0.388889 1 0.084746 0.125> * X]
+ 1 * < 0.222222 0.208333 0.338983 0.416667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
+ 1 * < 0.166667 0.166667 0.389831 0.375> * X]
- 0.265 * < 0.194444 0.416667 0.101695 0.041667> * X]
+ 0.3856 * < 0.194444 0.125 0.389831 0.375> * X]
+ 0.5122 * < 0.75 0.5 0.627119 0.541667> * X]
- 0.525 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.2145 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 0.0262
Number of support vectors: 10
Number of kernel evaluations: 1842 (65.749% cached)
Classifier for classes: setosa, virginica
BinarySMO
1 * < 0.166667 0.208333 0.59322 0.666667> * X]
- 0.8549 * < 0.388889 1 0.084746 0.125> * X]
+ 0.651 * < 1 0.75 0.915254 0.791667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
- 0.1612 * < 0.222222 0.541667 0.118644 0.166667> * X]
+ 0.1957 * < 0.472222 0.083333 0.677966 0.583333> * X]
- 0.1255 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.295 * < 0.944444 0.25 1 0.916667> * X]
+ 0.0856
Number of support vectors: 8
Number of kernel evaluations: 2113 (69.684% cached)
Classifier for classes: versicolor, virginica
BinarySMO
1 * < 0.555556 0.208333 0.677966 0.75> * X]
- 1 * < 0.305556 0.416667 0.59322 0.583333> * X]
- 1 * < 0.666667 0.458333 0.627119 0.583333> * X]
- 1 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 1 * < 0.444444 0.416667 0.694915 0.708333> * X]
- 1 * < 0.527778 0.083333 0.59322 0.583333> * X]
+ 0.3452 * < 1 0.75 0.915254 0.791667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
- 1 * < 0.472222 0.291667 0.694915 0.625> * X]
+ 0.7325 * < 0.555556 0.375 0.779661 0.708333> * X]
- 1 * < 0.666667 0.416667 0.677966 0.666667> * X]
- 0.2861 * < 0.75 0.5 0.627119 0.541667> * X]
+ 1 * < 0.611111 0.416667 0.762712 0.708333> * X]
- 1 * < 0.5 0.375 0.627119 0.541667> * X]
- 1 * < 0.722222 0.458333 0.661017 0.583333> * X]
+ 1 * < 0.472222 0.083333 0.677966 0.583333> * X]
+ 1 * < 0.583333 0.458333 0.762712 0.708333> * X]
+ 1 * < 0.611111 0.5 0.694915 0.791667> * X]
+ 1 * < 0.5 0.416667 0.661017 0.708333> * X]
- 1 * < 0.694444 0.333333 0.644068 0.541667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
+ 1 * < 0.527778 0.333333 0.644068 0.708333> * X]
- 1 * < 0.444444 0.5 0.644068 0.708333> * X]
+ 1 * < 0.5 0.25 0.779661 0.541667> * X]
+ 0.5817 * < 0.805556 0.5 0.847458 0.708333> * X]
+ 1 * < 0.555556 0.291667 0.661017 0.708333> * X]
+ 0.1465 * < 0.361111 0.333333 0.661017 0.791667> * X]
- 1 * < 0.555556 0.208333 0.661017 0.583333> * X]
- 1 * < 0.555556 0.125 0.576271 0.5> * X]
+ 1 * < 0.555556 0.333333 0.694915 0.583333> * X]
+ 1 * < 0.166667 0.208333 0.59322 0.666667> * X]
+ 1 * < 0.805556 0.416667 0.813559 0.625> * X]
- 1 * < 0.555556 0.541667 0.627119 0.625> * X]
+ 1 * < 0.472222 0.416667 0.644068 0.708333> * X]
- 1 * < 0.361111 0.416667 0.59322 0.583333> * X]
- 0.7282 * < 0.583333 0.5 0.59322 0.583333> * X]
- 0.7915 * < 0.333333 0.125 0.508475 0.5> * X]
- 1 * < 0.472222 0.375 0.59322 0.583333> * X]
- 1 * < 0.611111 0.333333 0.610169 0.583333> * X]
+ 0.0916
Number of support vectors: 39
Number of kernel evaluations: 3726 (76.576% cached)
Linear Regression Model
weight =
73.4538 * feed=linseed,soybean,meatmeal,casein,sunflower +
43.2552 * feed=meatmeal,casein,sunflower +
49.3409 * feed=casein,sunflower +
160.2
Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
Class
Variable control
======================
spontaneous -1.1972
induced -0.4181
Intercept 1.7078
Odds Ratios...
Class
Variable control
======================
spontaneous 0.302
induced 0.6583
SMO
Kernel used:
RBF Kernel: K(x,y) = exp(-2.0*(x-y)^2)
Classifier for classes: setosa, versicolor
BinarySMO
- 1 * < 0.222222 0.541667 0.118644 0.166667> * X]
- 0.3223 * < 0.388889 1 0.084746 0.125> * X]
+ 1 * < 0.222222 0.208333 0.338983 0.416667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
+ 1 * < 0.166667 0.166667 0.389831 0.375> * X]
- 0.265 * < 0.194444 0.416667 0.101695 0.041667> * X]
+ 0.3856 * < 0.194444 0.125 0.389831 0.375> * X]
+ 0.5122 * < 0.75 0.5 0.627119 0.541667> * X]
- 0.525 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.2145 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 0.0262
Number of support vectors: 10
Number of kernel evaluations: 1842 (65.749% cached)
Classifier for classes: setosa, virginica
BinarySMO
1 * < 0.166667 0.208333 0.59322 0.666667> * X]
- 0.8549 * < 0.388889 1 0.084746 0.125> * X]
+ 0.651 * < 1 0.75 0.915254 0.791667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
- 0.1612 * < 0.222222 0.541667 0.118644 0.166667> * X]
+ 0.1957 * < 0.472222 0.083333 0.677966 0.583333> * X]
- 0.1255 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.295 * < 0.944444 0.25 1 0.916667> * X]
+ 0.0856
Number of support vectors: 8
Number of kernel evaluations: 2113 (69.684% cached)
Classifier for classes: versicolor, virginica
BinarySMO
1 * < 0.555556 0.208333 0.677966 0.75> * X]
- 1 * < 0.305556 0.416667 0.59322 0.583333> * X]
- 1 * < 0.666667 0.458333 0.627119 0.583333> * X]
- 1 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 1 * < 0.444444 0.416667 0.694915 0.708333> * X]
- 1 * < 0.527778 0.083333 0.59322 0.583333> * X]
+ 0.3452 * < 1 0.75 0.915254 0.791667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
- 1 * < 0.472222 0.291667 0.694915 0.625> * X]
+ 0.7325 * < 0.555556 0.375 0.779661 0.708333> * X]
- 1 * < 0.666667 0.416667 0.677966 0.666667> * X]
- 0.2861 * < 0.75 0.5 0.627119 0.541667> * X]
+ 1 * < 0.611111 0.416667 0.762712 0.708333> * X]
- 1 * < 0.5 0.375 0.627119 0.541667> * X]
- 1 * < 0.722222 0.458333 0.661017 0.583333> * X]
+ 1 * < 0.472222 0.083333 0.677966 0.583333> * X]
+ 1 * < 0.583333 0.458333 0.762712 0.708333> * X]
+ 1 * < 0.611111 0.5 0.694915 0.791667> * X]
+ 1 * < 0.5 0.416667 0.661017 0.708333> * X]
- 1 * < 0.694444 0.333333 0.644068 0.541667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
+ 1 * < 0.527778 0.333333 0.644068 0.708333> * X]
- 1 * < 0.444444 0.5 0.644068 0.708333> * X]
+ 1 * < 0.5 0.25 0.779661 0.541667> * X]
+ 0.5817 * < 0.805556 0.5 0.847458 0.708333> * X]
+ 1 * < 0.555556 0.291667 0.661017 0.708333> * X]
+ 0.1465 * < 0.361111 0.333333 0.661017 0.791667> * X]
- 1 * < 0.555556 0.208333 0.661017 0.583333> * X]
- 1 * < 0.555556 0.125 0.576271 0.5> * X]
+ 1 * < 0.555556 0.333333 0.694915 0.583333> * X]
+ 1 * < 0.166667 0.208333 0.59322 0.666667> * X]
+ 1 * < 0.805556 0.416667 0.813559 0.625> * X]
- 1 * < 0.555556 0.541667 0.627119 0.625> * X]
+ 1 * < 0.472222 0.416667 0.644068 0.708333> * X]
- 1 * < 0.361111 0.416667 0.59322 0.583333> * X]
- 0.7282 * < 0.583333 0.5 0.59322 0.583333> * X]
- 0.7915 * < 0.333333 0.125 0.508475 0.5> * X]
- 1 * < 0.472222 0.375 0.59322 0.583333> * X]
- 1 * < 0.611111 0.333333 0.610169 0.583333> * X]
+ 0.0916
Number of support vectors: 39
Number of kernel evaluations: 3726 (76.576% cached)
Linear Regression Model
weight =
73.4538 * feed=linseed,soybean,meatmeal,casein,sunflower +
43.2552 * feed=meatmeal,casein,sunflower +
49.3409 * feed=casein,sunflower +
160.2
Logistic Regression with ridge parameter of 1.0E-8
Coefficients...
Class
Variable control
======================
spontaneous -1.1972
induced -0.4181
Intercept 1.7078
Odds Ratios...
Class
Variable control
======================
spontaneous 0.302
induced 0.6583
SMO
Kernel used:
RBF Kernel: K(x,y) = exp(-2.0*(x-y)^2)
Classifier for classes: setosa, versicolor
BinarySMO
- 1 * < 0.222222 0.541667 0.118644 0.166667> * X]
- 0.3223 * < 0.388889 1 0.084746 0.125> * X]
+ 1 * < 0.222222 0.208333 0.338983 0.416667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
+ 1 * < 0.166667 0.166667 0.389831 0.375> * X]
- 0.265 * < 0.194444 0.416667 0.101695 0.041667> * X]
+ 0.3856 * < 0.194444 0.125 0.389831 0.375> * X]
+ 0.5122 * < 0.75 0.5 0.627119 0.541667> * X]
- 0.525 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.2145 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 0.0262
Number of support vectors: 10
Number of kernel evaluations: 1842 (65.749% cached)
Classifier for classes: setosa, virginica
BinarySMO
1 * < 0.166667 0.208333 0.59322 0.666667> * X]
- 0.8549 * < 0.388889 1 0.084746 0.125> * X]
+ 0.651 * < 1 0.75 0.915254 0.791667> * X]
- 1 * < 0.055556 0.125 0.050847 0.083333> * X]
- 0.1612 * < 0.222222 0.541667 0.118644 0.166667> * X]
+ 0.1957 * < 0.472222 0.083333 0.677966 0.583333> * X]
- 0.1255 * < 0.194444 0.625 0.101695 0.208333> * X]
+ 0.295 * < 0.944444 0.25 1 0.916667> * X]
+ 0.0856
Number of support vectors: 8
Number of kernel evaluations: 2113 (69.684% cached)
Classifier for classes: versicolor, virginica
BinarySMO
1 * < 0.555556 0.208333 0.677966 0.75> * X]
- 1 * < 0.305556 0.416667 0.59322 0.583333> * X]
- 1 * < 0.666667 0.458333 0.627119 0.583333> * X]
- 1 * < 0.472222 0.583333 0.59322 0.625> * X]
+ 1 * < 0.444444 0.416667 0.694915 0.708333> * X]
- 1 * < 0.527778 0.083333 0.59322 0.583333> * X]
+ 0.3452 * < 1 0.75 0.915254 0.791667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
- 1 * < 0.472222 0.291667 0.694915 0.625> * X]
+ 0.7325 * < 0.555556 0.375 0.779661 0.708333> * X]
- 1 * < 0.666667 0.416667 0.677966 0.666667> * X]
- 0.2861 * < 0.75 0.5 0.627119 0.541667> * X]
+ 1 * < 0.611111 0.416667 0.762712 0.708333> * X]
- 1 * < 0.5 0.375 0.627119 0.541667> * X]
- 1 * < 0.722222 0.458333 0.661017 0.583333> * X]
+ 1 * < 0.472222 0.083333 0.677966 0.583333> * X]
+ 1 * < 0.583333 0.458333 0.762712 0.708333> * X]
+ 1 * < 0.611111 0.5 0.694915 0.791667> * X]
+ 1 * < 0.5 0.416667 0.661017 0.708333> * X]
- 1 * < 0.694444 0.333333 0.644068 0.541667> * X]
+ 1 * < 0.416667 0.291667 0.694915 0.75> * X]
+ 1 * < 0.527778 0.333333 0.644068 0.708333> * X]
- 1 * < 0.444444 0.5 0.644068 0.708333> * X]
+ 1 * < 0.5 0.25 0.779661 0.541667> * X]
+ 0.5817 * < 0.805556 0.5 0.847458 0.708333> * X]
+ 1 * < 0.555556 0.291667 0.661017 0.708333> * X]
+ 0.1465 * < 0.361111 0.333333 0.661017 0.791667> * X]
- 1 * < 0.555556 0.208333 0.661017 0.583333> * X]
- 1 * < 0.555556 0.125 0.576271 0.5> * X]
+ 1 * < 0.555556 0.333333 0.694915 0.583333> * X]
+ 1 * < 0.166667 0.208333 0.59322 0.666667> * X]
+ 1 * < 0.805556 0.416667 0.813559 0.625> * X]
- 1 * < 0.555556 0.541667 0.627119 0.625> * X]
+ 1 * < 0.472222 0.416667 0.644068 0.708333> * X]
- 1 * < 0.361111 0.416667 0.59322 0.583333> * X]
- 0.7282 * < 0.583333 0.5 0.59322 0.583333> * X]
- 0.7915 * < 0.333333 0.125 0.508475 0.5> * X]
- 1 * < 0.472222 0.375 0.59322 0.583333> * X]
- 1 * < 0.611111 0.333333 0.610169 0.583333> * X]
+ 0.0916
Number of support vectors: 39
Number of kernel evaluations: 3726 (76.576% cached)
IB1 instance-based classifier
using 1 nearest neighbour(s) for classification
AdaBoostM1: Base classifiers and their weights:
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.5 0.5
Petal.Length is missing
setosa versicolor virginica
0.3333333333333333 0.3333333333333333 0.3333333333333333
Weight: 0.69
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.3333333333333333 0.6666666666666667
Petal.Length is missing
setosa versicolor virginica
0.25 0.25 0.5000000000000001
Weight: 1.1
Decision Stump
Classifications
Petal.Width <= 1.75 : versicolor
Petal.Width > 1.75 : virginica
Petal.Width is missing : versicolor
Class distributions
Petal.Width <= 1.75
setosa versicolor virginica
0.24154589371980675 0.7101449275362319 0.04830917874396136
Petal.Width > 1.75
setosa versicolor virginica
0.0 0.032258064516129024 0.967741935483871
Petal.Width is missing
setosa versicolor virginica
0.16666666666666666 0.5 0.33333333333333337
Weight: 1.32
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
2.4541772123292936E-17 0.5536309127248501 0.44636908727514996
Petal.Length is missing
setosa versicolor virginica
0.3968253968253968 0.33393610608800484 0.2692384970865984
Weight: 1.0
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
4.876852104094113E-17 0.31364408378939285 0.6863559162106071
Petal.Length is missing
setosa versicolor virginica
0.27151498487764564 0.22848501512235284 0.5000000000000016
Weight: 1.22
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-1.2574765214077044E-17 0.6067682992755968 0.3932317007244033
Petal.Length is missing
setosa versicolor virginica
0.17596222380612905 0.4999999999999999 0.32403777619387103
Weight: 0.74
Decision Stump
Classifications
Petal.Length <= 4.85 : versicolor
Petal.Length > 4.85 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 4.85
setosa versicolor virginica
0.2517002096709582 0.660989158028418 0.08731063230062384
Petal.Length > 4.85
setosa versicolor virginica
1.379474925594973E-17 0.058064332648205416 0.9419356673517946
Petal.Length is missing
setosa versicolor virginica
0.1301568472386974 0.3698431527613032 0.49999999999999944
Weight: 1.37
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-2.17703546955402E-18 0.4168896637524031 0.5831103362475969
Petal.Length is missing
setosa versicolor virginica
0.32003984920368916 0.28346835863050734 0.39649179216580344
Weight: 0.93
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
3.621292691813958E-17 0.6437704901626135 0.35622950983738655
Petal.Length is missing
setosa versicolor virginica
0.2233256919345557 0.4999999999999997 0.2766743080654446
Weight: 0.96
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
1.1378476401045689E-17 0.4087218990793083 0.5912781009206917
Petal.Length is missing
setosa versicolor virginica
0.15437422894330288 0.3456257710566977 0.4999999999999995
Weight: 0.64
Number of performed Iterations: 10
Bagging with 10 iterations and base learner
weka.classifiers.trees.REPTree -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0
LogitBoost: Base classifiers and their weights:
Iteration 1
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 3.0000000000000027
Petal.Length > 2.45 : -1.5
Petal.Length is missing : 1.9599137128049413E-15
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 0.8 : -1.5000000000000013
Petal.Width > 0.8 : 0.7500000000000003
Petal.Width is missing : -7.608728462097734E-16
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -1.2836538461538467
Petal.Width > 1.75 : 2.9021739130434803
Petal.Width is missing : 1.6283271027835617E-15
Iteration 2
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.107298632804185
Petal.Length > 2.45 : -1.1494279893253192
Petal.Length is missing : -0.3957206900169236
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 4.95 : 0.8927072824773201
Petal.Length > 4.95 : -1.281877805427337
Petal.Length is missing : 0.13310268365013825
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 4.85 : -1.0114659062621487
Petal.Length > 4.85 : 1.3395195188507139
Petal.Length is missing : 0.12532848791571008
Iteration 3
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0571563837220312
Petal.Width > 0.8 : -1.0537431091378222
Petal.Width is missing : -0.12564217216283943
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.65 : 0.35336467860151544
Petal.Width > 1.65 : -0.8272889203902701
Petal.Width is missing : 0.021646893923887404
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.65 : -0.5554268198088237
Petal.Width > 1.65 : 0.9085259163893444
Petal.Width is missing : 0.057019234645703
Iteration 4
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0317708097109874
Petal.Length > 2.45 : -1.025782418471695
Petal.Length is missing : 0.028704205441294997
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.2032301693639639
Petal.Length > 5.15 : -1.6167505374155677
Petal.Length is missing : -0.029665612140833936
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.3574866748606593
Petal.Length > 5.15 : 1.6858123253924724
Petal.Length is missing : 0.02407415847193774
Iteration 5
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0174288460889573
Petal.Width > 0.8 : -1.0121734540399154
Petal.Width is missing : 0.14037130816438537
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.8085139247966666
Sepal.Length > 4.95 : 0.17385428460727828
Sepal.Length is missing : -0.07058499672025116
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 2.8499999999999996 : 0.7604017682287972
Sepal.Width > 2.8499999999999996 : -0.9425445176141151
Sepal.Width is missing : 0.036618686875232266
Iteration 6
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0082467446058936
Petal.Length > 2.45 : -1.0070149682146894
Petal.Length is missing : 0.007908072736191237
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.45 : 0.5557671865473288
Petal.Width > 1.45 : -0.3122484934314317
Petal.Width is missing : -0.005865013040006491
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.35 : -1.0490610677454482
Petal.Width > 1.35 : 0.3286632987456759
Petal.Width is missing : 0.005223499537758138
Iteration 7
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0059118577482615
Petal.Width > 0.8 : -1.003405443224416
Petal.Width is missing : 0.23621249641952188
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 6.55 : -0.07598465327608493
Sepal.Length > 6.55 : 1.1183107264322862
Sepal.Length is missing : 0.12302823999845726
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 3.1500000000000004 : 0.04554114749362306
Sepal.Width > 3.1500000000000004 : -1.9197981583325392
Sepal.Width is missing : -0.16064487947532546
Iteration 8
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.002838400313738
Petal.Length > 2.45 : -1.0016483326432501
Petal.Length is missing : 0.1949327218316983
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.10428326648854233
Petal.Length > 5.15 : -1.1835612905032828
Petal.Length is missing : -0.06263074067522303
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.12453497884995612
Petal.Length > 5.15 : 1.184599934085556
Petal.Length is missing : 0.05459419319287985
Iteration 9
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.001558831849546
Petal.Width > 0.8 : -1.000847094753804
Petal.Width is missing : 0.27087980423892677
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.3433092354251668
Sepal.Length > 4.95 : 0.05737731913335332
Sepal.Length is missing : -0.07412404358407221
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Length <= 4.95 : 1.3690177365198544
Sepal.Length > 4.95 : -0.06609761928681071
Sepal.Length is missing : 0.06745461045228492
Iteration 10
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0008685918988107
Petal.Length > 2.45 : -1.0004573371926975
Petal.Length is missing : 0.25063512727922765
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.75 : 0.3702482917142169
Petal.Width > 1.75 : -0.7012067772823177
Petal.Width is missing : 0.018104326215643476
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -0.3841943142699475
Petal.Width > 1.75 : 0.7027054245666945
Petal.Width is missing : -0.02226933911584284
Number of performed iterations: 10
Stacking
Base classifiers
ZeroR predicts class value: setosa
Meta classifier
ZeroR predicts class value: setosa
AdaBoostM1: Base classifiers and their weights:
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.5 0.5
Petal.Length is missing
setosa versicolor virginica
0.3333333333333333 0.3333333333333333 0.3333333333333333
Weight: 0.69
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.3333333333333333 0.6666666666666667
Petal.Length is missing
setosa versicolor virginica
0.25 0.25 0.5000000000000001
Weight: 1.1
Decision Stump
Classifications
Petal.Width <= 1.75 : versicolor
Petal.Width > 1.75 : virginica
Petal.Width is missing : versicolor
Class distributions
Petal.Width <= 1.75
setosa versicolor virginica
0.24154589371980675 0.7101449275362319 0.04830917874396136
Petal.Width > 1.75
setosa versicolor virginica
0.0 0.032258064516129024 0.967741935483871
Petal.Width is missing
setosa versicolor virginica
0.16666666666666666 0.5 0.33333333333333337
Weight: 1.32
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
2.4541772123292936E-17 0.5536309127248501 0.44636908727514996
Petal.Length is missing
setosa versicolor virginica
0.3968253968253968 0.33393610608800484 0.2692384970865984
Weight: 1.0
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
4.876852104094113E-17 0.31364408378939285 0.6863559162106071
Petal.Length is missing
setosa versicolor virginica
0.27151498487764564 0.22848501512235284 0.5000000000000016
Weight: 1.22
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-1.2574765214077044E-17 0.6067682992755968 0.3932317007244033
Petal.Length is missing
setosa versicolor virginica
0.17596222380612905 0.4999999999999999 0.32403777619387103
Weight: 0.74
Decision Stump
Classifications
Petal.Length <= 4.85 : versicolor
Petal.Length > 4.85 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 4.85
setosa versicolor virginica
0.2517002096709582 0.660989158028418 0.08731063230062384
Petal.Length > 4.85
setosa versicolor virginica
1.379474925594973E-17 0.058064332648205416 0.9419356673517946
Petal.Length is missing
setosa versicolor virginica
0.1301568472386974 0.3698431527613032 0.49999999999999944
Weight: 1.37
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-2.17703546955402E-18 0.4168896637524031 0.5831103362475969
Petal.Length is missing
setosa versicolor virginica
0.32003984920368916 0.28346835863050734 0.39649179216580344
Weight: 0.93
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
3.621292691813958E-17 0.6437704901626135 0.35622950983738655
Petal.Length is missing
setosa versicolor virginica
0.2233256919345557 0.4999999999999997 0.2766743080654446
Weight: 0.96
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
1.1378476401045689E-17 0.4087218990793083 0.5912781009206917
Petal.Length is missing
setosa versicolor virginica
0.15437422894330288 0.3456257710566977 0.4999999999999995
Weight: 0.64
Number of performed Iterations: 10
Bagging with 10 iterations and base learner
weka.classifiers.trees.REPTree -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0
LogitBoost: Base classifiers and their weights:
Iteration 1
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 3.0000000000000027
Petal.Length > 2.45 : -1.5
Petal.Length is missing : 1.9599137128049413E-15
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 0.8 : -1.5000000000000013
Petal.Width > 0.8 : 0.7500000000000003
Petal.Width is missing : -7.608728462097734E-16
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -1.2836538461538467
Petal.Width > 1.75 : 2.9021739130434803
Petal.Width is missing : 1.6283271027835617E-15
Iteration 2
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.107298632804185
Petal.Length > 2.45 : -1.1494279893253192
Petal.Length is missing : -0.3957206900169236
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 4.95 : 0.8927072824773201
Petal.Length > 4.95 : -1.281877805427337
Petal.Length is missing : 0.13310268365013825
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 4.85 : -1.0114659062621487
Petal.Length > 4.85 : 1.3395195188507139
Petal.Length is missing : 0.12532848791571008
Iteration 3
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0571563837220312
Petal.Width > 0.8 : -1.0537431091378222
Petal.Width is missing : -0.12564217216283943
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.65 : 0.35336467860151544
Petal.Width > 1.65 : -0.8272889203902701
Petal.Width is missing : 0.021646893923887404
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.65 : -0.5554268198088237
Petal.Width > 1.65 : 0.9085259163893444
Petal.Width is missing : 0.057019234645703
Iteration 4
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0317708097109874
Petal.Length > 2.45 : -1.025782418471695
Petal.Length is missing : 0.028704205441294997
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.2032301693639639
Petal.Length > 5.15 : -1.6167505374155677
Petal.Length is missing : -0.029665612140833936
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.3574866748606593
Petal.Length > 5.15 : 1.6858123253924724
Petal.Length is missing : 0.02407415847193774
Iteration 5
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0174288460889573
Petal.Width > 0.8 : -1.0121734540399154
Petal.Width is missing : 0.14037130816438537
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.8085139247966666
Sepal.Length > 4.95 : 0.17385428460727828
Sepal.Length is missing : -0.07058499672025116
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 2.8499999999999996 : 0.7604017682287972
Sepal.Width > 2.8499999999999996 : -0.9425445176141151
Sepal.Width is missing : 0.036618686875232266
Iteration 6
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0082467446058936
Petal.Length > 2.45 : -1.0070149682146894
Petal.Length is missing : 0.007908072736191237
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.45 : 0.5557671865473288
Petal.Width > 1.45 : -0.3122484934314317
Petal.Width is missing : -0.005865013040006491
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.35 : -1.0490610677454482
Petal.Width > 1.35 : 0.3286632987456759
Petal.Width is missing : 0.005223499537758138
Iteration 7
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0059118577482615
Petal.Width > 0.8 : -1.003405443224416
Petal.Width is missing : 0.23621249641952188
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 6.55 : -0.07598465327608493
Sepal.Length > 6.55 : 1.1183107264322862
Sepal.Length is missing : 0.12302823999845726
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 3.1500000000000004 : 0.04554114749362306
Sepal.Width > 3.1500000000000004 : -1.9197981583325392
Sepal.Width is missing : -0.16064487947532546
Iteration 8
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.002838400313738
Petal.Length > 2.45 : -1.0016483326432501
Petal.Length is missing : 0.1949327218316983
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.10428326648854233
Petal.Length > 5.15 : -1.1835612905032828
Petal.Length is missing : -0.06263074067522303
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.12453497884995612
Petal.Length > 5.15 : 1.184599934085556
Petal.Length is missing : 0.05459419319287985
Iteration 9
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.001558831849546
Petal.Width > 0.8 : -1.000847094753804
Petal.Width is missing : 0.27087980423892677
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.3433092354251668
Sepal.Length > 4.95 : 0.05737731913335332
Sepal.Length is missing : -0.07412404358407221
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Length <= 4.95 : 1.3690177365198544
Sepal.Length > 4.95 : -0.06609761928681071
Sepal.Length is missing : 0.06745461045228492
Iteration 10
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0008685918988107
Petal.Length > 2.45 : -1.0004573371926975
Petal.Length is missing : 0.25063512727922765
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.75 : 0.3702482917142169
Petal.Width > 1.75 : -0.7012067772823177
Petal.Width is missing : 0.018104326215643476
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -0.3841943142699475
Petal.Width > 1.75 : 0.7027054245666945
Petal.Width is missing : -0.02226933911584284
Number of performed iterations: 10
Stacking
Base classifiers
ZeroR predicts class value: setosa
Meta classifier
ZeroR predicts class value: setosa
AdaBoostM1: Base classifiers and their weights:
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.5 0.5
Petal.Length is missing
setosa versicolor virginica
0.3333333333333333 0.3333333333333333 0.3333333333333333
Weight: 0.69
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
0.0 0.3333333333333333 0.6666666666666667
Petal.Length is missing
setosa versicolor virginica
0.25 0.25 0.5000000000000001
Weight: 1.1
Decision Stump
Classifications
Petal.Width <= 1.75 : versicolor
Petal.Width > 1.75 : virginica
Petal.Width is missing : versicolor
Class distributions
Petal.Width <= 1.75
setosa versicolor virginica
0.24154589371980675 0.7101449275362319 0.04830917874396136
Petal.Width > 1.75
setosa versicolor virginica
0.0 0.032258064516129024 0.967741935483871
Petal.Width is missing
setosa versicolor virginica
0.16666666666666666 0.5 0.33333333333333337
Weight: 1.32
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : setosa
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
2.4541772123292936E-17 0.5536309127248501 0.44636908727514996
Petal.Length is missing
setosa versicolor virginica
0.3968253968253968 0.33393610608800484 0.2692384970865984
Weight: 1.0
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
4.876852104094113E-17 0.31364408378939285 0.6863559162106071
Petal.Length is missing
setosa versicolor virginica
0.27151498487764564 0.22848501512235284 0.5000000000000016
Weight: 1.22
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-1.2574765214077044E-17 0.6067682992755968 0.3932317007244033
Petal.Length is missing
setosa versicolor virginica
0.17596222380612905 0.4999999999999999 0.32403777619387103
Weight: 0.74
Decision Stump
Classifications
Petal.Length <= 4.85 : versicolor
Petal.Length > 4.85 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 4.85
setosa versicolor virginica
0.2517002096709582 0.660989158028418 0.08731063230062384
Petal.Length > 4.85
setosa versicolor virginica
1.379474925594973E-17 0.058064332648205416 0.9419356673517946
Petal.Length is missing
setosa versicolor virginica
0.1301568472386974 0.3698431527613032 0.49999999999999944
Weight: 1.37
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
-2.17703546955402E-18 0.4168896637524031 0.5831103362475969
Petal.Length is missing
setosa versicolor virginica
0.32003984920368916 0.28346835863050734 0.39649179216580344
Weight: 0.93
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : versicolor
Petal.Length is missing : versicolor
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
3.621292691813958E-17 0.6437704901626135 0.35622950983738655
Petal.Length is missing
setosa versicolor virginica
0.2233256919345557 0.4999999999999997 0.2766743080654446
Weight: 0.96
Decision Stump
Classifications
Petal.Length <= 2.45 : setosa
Petal.Length > 2.45 : virginica
Petal.Length is missing : virginica
Class distributions
Petal.Length <= 2.45
setosa versicolor virginica
1.0 0.0 0.0
Petal.Length > 2.45
setosa versicolor virginica
1.1378476401045689E-17 0.4087218990793083 0.5912781009206917
Petal.Length is missing
setosa versicolor virginica
0.15437422894330288 0.3456257710566977 0.4999999999999995
Weight: 0.64
Number of performed Iterations: 10
Bagging with 10 iterations and base learner
weka.classifiers.trees.REPTree -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0
LogitBoost: Base classifiers and their weights:
Iteration 1
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 3.0000000000000027
Petal.Length > 2.45 : -1.5
Petal.Length is missing : 1.9599137128049413E-15
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 0.8 : -1.5000000000000013
Petal.Width > 0.8 : 0.7500000000000003
Petal.Width is missing : -7.608728462097734E-16
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -1.2836538461538467
Petal.Width > 1.75 : 2.9021739130434803
Petal.Width is missing : 1.6283271027835617E-15
Iteration 2
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.107298632804185
Petal.Length > 2.45 : -1.1494279893253192
Petal.Length is missing : -0.3957206900169236
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 4.95 : 0.8927072824773201
Petal.Length > 4.95 : -1.281877805427337
Petal.Length is missing : 0.13310268365013825
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 4.85 : -1.0114659062621487
Petal.Length > 4.85 : 1.3395195188507139
Petal.Length is missing : 0.12532848791571008
Iteration 3
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0571563837220312
Petal.Width > 0.8 : -1.0537431091378222
Petal.Width is missing : -0.12564217216283943
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.65 : 0.35336467860151544
Petal.Width > 1.65 : -0.8272889203902701
Petal.Width is missing : 0.021646893923887404
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.65 : -0.5554268198088237
Petal.Width > 1.65 : 0.9085259163893444
Petal.Width is missing : 0.057019234645703
Iteration 4
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0317708097109874
Petal.Length > 2.45 : -1.025782418471695
Petal.Length is missing : 0.028704205441294997
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.2032301693639639
Petal.Length > 5.15 : -1.6167505374155677
Petal.Length is missing : -0.029665612140833936
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.3574866748606593
Petal.Length > 5.15 : 1.6858123253924724
Petal.Length is missing : 0.02407415847193774
Iteration 5
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0174288460889573
Petal.Width > 0.8 : -1.0121734540399154
Petal.Width is missing : 0.14037130816438537
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.8085139247966666
Sepal.Length > 4.95 : 0.17385428460727828
Sepal.Length is missing : -0.07058499672025116
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 2.8499999999999996 : 0.7604017682287972
Sepal.Width > 2.8499999999999996 : -0.9425445176141151
Sepal.Width is missing : 0.036618686875232266
Iteration 6
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0082467446058936
Petal.Length > 2.45 : -1.0070149682146894
Petal.Length is missing : 0.007908072736191237
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.45 : 0.5557671865473288
Petal.Width > 1.45 : -0.3122484934314317
Petal.Width is missing : -0.005865013040006491
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.35 : -1.0490610677454482
Petal.Width > 1.35 : 0.3286632987456759
Petal.Width is missing : 0.005223499537758138
Iteration 7
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.0059118577482615
Petal.Width > 0.8 : -1.003405443224416
Petal.Width is missing : 0.23621249641952188
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 6.55 : -0.07598465327608493
Sepal.Length > 6.55 : 1.1183107264322862
Sepal.Length is missing : 0.12302823999845726
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Width <= 3.1500000000000004 : 0.04554114749362306
Sepal.Width > 3.1500000000000004 : -1.9197981583325392
Sepal.Width is missing : -0.16064487947532546
Iteration 8
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.002838400313738
Petal.Length > 2.45 : -1.0016483326432501
Petal.Length is missing : 0.1949327218316983
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Length <= 5.15 : 0.10428326648854233
Petal.Length > 5.15 : -1.1835612905032828
Petal.Length is missing : -0.06263074067522303
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Length <= 5.15 : -0.12453497884995612
Petal.Length > 5.15 : 1.184599934085556
Petal.Length is missing : 0.05459419319287985
Iteration 9
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Width <= 0.8 : 1.001558831849546
Petal.Width > 0.8 : -1.000847094753804
Petal.Width is missing : 0.27087980423892677
Class 2 (Species=versicolor)
Decision Stump
Classifications
Sepal.Length <= 4.95 : -1.3433092354251668
Sepal.Length > 4.95 : 0.05737731913335332
Sepal.Length is missing : -0.07412404358407221
Class 3 (Species=virginica)
Decision Stump
Classifications
Sepal.Length <= 4.95 : 1.3690177365198544
Sepal.Length > 4.95 : -0.06609761928681071
Sepal.Length is missing : 0.06745461045228492
Iteration 10
Class 1 (Species=setosa)
Decision Stump
Classifications
Petal.Length <= 2.45 : 1.0008685918988107
Petal.Length > 2.45 : -1.0004573371926975
Petal.Length is missing : 0.25063512727922765
Class 2 (Species=versicolor)
Decision Stump
Classifications
Petal.Width <= 1.75 : 0.3702482917142169
Petal.Width > 1.75 : -0.7012067772823177
Petal.Width is missing : 0.018104326215643476
Class 3 (Species=virginica)
Decision Stump
Classifications
Petal.Width <= 1.75 : -0.3841943142699475
Petal.Width > 1.75 : 0.7027054245666945
Petal.Width is missing : -0.02226933911584284
Number of performed iterations: 10
Stacking
Base classifiers
ZeroR predicts class value: setosa
Meta classifier
ZeroR predicts class value: setosa
JRIP rules:
===========
(Petal.Length <= 1.9) => Species=setosa (50.0/0.0)
(Petal.Width <= 1.7) and (Petal.Length <= 4.9) => Species=versicolor (48.0/1.0)
=> Species=virginica (52.0/3.0)
Number of Rules : 3
M5 pruned model rules
(using smoothed linear models) :
Number of Rules : 2
Rule: 1
IF
cyl > 5
THEN
mpg =
-0.5389 * cyl
+ 0.0048 * disp
- 0.0206 * hp
- 3.0997 * wt
+ 34.4212 [21/26.733%]
Rule: 2
mpg =
-0.1351 * disp
+ 40.872 [11/59.295%]
Petal.Width:
< 0.8 -> setosa
< 1.75 -> versicolor
>= 1.75 -> virginica
(144/150 instances correct)
PART decision list
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width <= 1.7 AND
Petal.Length <= 4.9: versicolor (48.0/1.0)
: virginica (52.0/3.0)
Number of Rules : 3
JRIP rules:
===========
(Petal.Length <= 1.9) => Species=setosa (50.0/0.0)
(Petal.Width <= 1.7) and (Petal.Length <= 4.9) => Species=versicolor (48.0/1.0)
=> Species=virginica (52.0/3.0)
Number of Rules : 3
M5 pruned model rules
(using smoothed linear models) :
Number of Rules : 2
Rule: 1
IF
cyl > 5
THEN
mpg =
-0.5389 * cyl
+ 0.0048 * disp
- 0.0206 * hp
- 3.0997 * wt
+ 34.4212 [21/26.733%]
Rule: 2
mpg =
-0.1351 * disp
+ 40.872 [11/59.295%]
Petal.Width:
< 0.8 -> setosa
< 1.75 -> versicolor
>= 1.75 -> virginica
(144/150 instances correct)
PART decision list
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width <= 1.7 AND
Petal.Length <= 4.9: versicolor (48.0/1.0)
: virginica (52.0/3.0)
Number of Rules : 3
JRIP rules:
===========
(Petal.Length <= 1.9) => Species=setosa (50.0/0.0)
(Petal.Width <= 1.7) and (Petal.Length <= 4.9) => Species=versicolor (48.0/1.0)
=> Species=virginica (52.0/3.0)
Number of Rules : 3
M5 pruned model rules
(using smoothed linear models) :
Number of Rules : 2
Rule: 1
IF
cyl > 5
THEN
mpg =
-0.5389 * cyl
+ 0.0048 * disp
- 0.0206 * hp
- 3.0997 * wt
+ 34.4212 [21/26.733%]
Rule: 2
mpg =
-0.1351 * disp
+ 40.872 [11/59.295%]
Petal.Width:
< 0.8 -> setosa
< 1.75 -> versicolor
>= 1.75 -> virginica
(144/150 instances correct)
PART decision list
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width <= 1.7 AND
Petal.Length <= 4.9: versicolor (48.0/1.0)
: virginica (52.0/3.0)
Number of Rules : 3
Decision Stump
Classifications
outlook = overcast : yes
outlook != overcast : yes
outlook is missing : yes
Class distributions
outlook = overcast
yes no
1.0 0.0
outlook != overcast
yes no
0.5 0.5
outlook is missing
yes no
0.6428571428571429 0.35714285714285715
J48 pruned tree
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
| Petal.Width <= 1.7
| | Petal.Length <= 4.9: versicolor (48.0/1.0)
| | Petal.Length > 4.9
| | | Petal.Width <= 1.5: virginica (3.0)
| | | Petal.Width > 1.5: versicolor (3.0/1.0)
| Petal.Width > 1.7: virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Logistic model tree
------------------
: LM_1:11/11 (14)
Number of Leaves : 1
Size of the Tree : 1
LM_1:
Class yes :
6.95 +
[outlook=sunny] * -0.65 +
[outlook=overcast] * 2.82 +
[temperature] * -0.02 +
[humidity] * -0.06 +
[windy=TRUE] * -1.38
Class no :
-6.95 +
[outlook=sunny] * 0.65 +
[outlook=overcast] * -2.82 +
[temperature] * 0.02 +
[humidity] * 0.06 +
[windy=TRUE] * 1.38
M5 pruned model tree:
(using smoothed linear models)
CHMIN <= 7.5 :
| MMAX <= 6100 : LM1 (76/7.046%)
| MMAX > 6100 :
| | CACH <= 28 :
| | | CACH <= 0.5 : LM2 (20/7.795%)
| | | CACH > 0.5 :
| | | | CHMIN <= 3.5 : LM3 (34/11.658%)
| | | | CHMIN > 3.5 : LM4 (9/12.866%)
| | CACH > 28 : LM5 (26/19.269%)
CHMIN > 7.5 :
| MMAX <= 28000 :
| | MMAX <= 13240 :
| | | CACH <= 81.5 : LM6 (6/18.551%)
| | | CACH > 81.5 : LM7 (4/30.824%)
| | MMAX > 13240 : LM8 (11/24.185%)
| MMAX > 28000 : LM9 (23/48.302%)
LM num: 1
class =
0.0025 * MMIN
+ 0.004 * MMAX
+ 0.0009 * CACH
+ 0.1758 * CHMIN
+ 0.205 * CHMAX
+ 7.6605
LM num: 2
class =
-0.012 * MYCT
- 0.0141 * MMIN
+ 0.0013 * MMAX
+ 0.0017 * CACH
+ 0.2793 * CHMIN
+ 0.6771 * CHMAX
+ 26.4438
LM num: 3
class =
-0.0096 * MMIN
+ 0.0028 * MMAX
+ 0.0018 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 24.6136
LM num: 4
class =
-0.0406 * MYCT
- 0.0096 * MMIN
+ 0.0033 * MMAX
- 0.294 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 38.0329
LM num: 5
class =
-0.0077 * MMIN
+ 0.0024 * MMAX
+ 0.3125 * CACH
+ 0.3866 * CHMIN
+ 0.5207 * CHMAX
+ 17.2559
LM num: 6
class =
-0.5146 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.3906 * CACH
- 2.0512 * CHMIN
+ 70.8672
LM num: 7
class =
-0.5896 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.4034 * CACH
- 2.0512 * CHMIN
+ 83.0016
LM num: 8
class =
-0.3653 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.2587 * CACH
- 1.957 * CHMIN
+ 82.5725
LM num: 9
class =
-0.4748 * MMIN
+ 0.0083 * MMAX
+ 0.003 * CACH
- 0.9387 * CHMIN
+ 2.2489 * CHMAX
- 51.8474
Number of Rules : 9
Decision Stump
Classifications
outlook = overcast : yes
outlook != overcast : yes
outlook is missing : yes
Class distributions
outlook = overcast
yes no
1.0 0.0
outlook != overcast
yes no
0.5 0.5
outlook is missing
yes no
0.6428571428571429 0.35714285714285715
J48 pruned tree
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
| Petal.Width <= 1.7
| | Petal.Length <= 4.9: versicolor (48.0/1.0)
| | Petal.Length > 4.9
| | | Petal.Width <= 1.5: virginica (3.0)
| | | Petal.Width > 1.5: versicolor (3.0/1.0)
| Petal.Width > 1.7: virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Logistic model tree
------------------
: LM_1:11/11 (14)
Number of Leaves : 1
Size of the Tree : 1
LM_1:
Class yes :
6.95 +
[outlook=sunny] * -0.65 +
[outlook=overcast] * 2.82 +
[temperature] * -0.02 +
[humidity] * -0.06 +
[windy=TRUE] * -1.38
Class no :
-6.95 +
[outlook=sunny] * 0.65 +
[outlook=overcast] * -2.82 +
[temperature] * 0.02 +
[humidity] * 0.06 +
[windy=TRUE] * 1.38
M5 pruned model tree:
(using smoothed linear models)
CHMIN <= 7.5 :
| MMAX <= 6100 : LM1 (76/7.046%)
| MMAX > 6100 :
| | CACH <= 28 :
| | | CACH <= 0.5 : LM2 (20/7.795%)
| | | CACH > 0.5 :
| | | | CHMIN <= 3.5 : LM3 (34/11.658%)
| | | | CHMIN > 3.5 : LM4 (9/12.866%)
| | CACH > 28 : LM5 (26/19.269%)
CHMIN > 7.5 :
| MMAX <= 28000 :
| | MMAX <= 13240 :
| | | CACH <= 81.5 : LM6 (6/18.551%)
| | | CACH > 81.5 : LM7 (4/30.824%)
| | MMAX > 13240 : LM8 (11/24.185%)
| MMAX > 28000 : LM9 (23/48.302%)
LM num: 1
class =
0.0025 * MMIN
+ 0.004 * MMAX
+ 0.0009 * CACH
+ 0.1758 * CHMIN
+ 0.205 * CHMAX
+ 7.6605
LM num: 2
class =
-0.012 * MYCT
- 0.0141 * MMIN
+ 0.0013 * MMAX
+ 0.0017 * CACH
+ 0.2793 * CHMIN
+ 0.6771 * CHMAX
+ 26.4438
LM num: 3
class =
-0.0096 * MMIN
+ 0.0028 * MMAX
+ 0.0018 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 24.6136
LM num: 4
class =
-0.0406 * MYCT
- 0.0096 * MMIN
+ 0.0033 * MMAX
- 0.294 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 38.0329
LM num: 5
class =
-0.0077 * MMIN
+ 0.0024 * MMAX
+ 0.3125 * CACH
+ 0.3866 * CHMIN
+ 0.5207 * CHMAX
+ 17.2559
LM num: 6
class =
-0.5146 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.3906 * CACH
- 2.0512 * CHMIN
+ 70.8672
LM num: 7
class =
-0.5896 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.4034 * CACH
- 2.0512 * CHMIN
+ 83.0016
LM num: 8
class =
-0.3653 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.2587 * CACH
- 1.957 * CHMIN
+ 82.5725
LM num: 9
class =
-0.4748 * MMIN
+ 0.0083 * MMAX
+ 0.003 * CACH
- 0.9387 * CHMIN
+ 2.2489 * CHMAX
- 51.8474
Number of Rules : 9
Decision Stump
Classifications
outlook = overcast : yes
outlook != overcast : yes
outlook is missing : yes
Class distributions
outlook = overcast
yes no
1.0 0.0
outlook != overcast
yes no
0.5 0.5
outlook is missing
yes no
0.6428571428571429 0.35714285714285715
J48 pruned tree
------------------
Petal.Width <= 0.6: setosa (50.0)
Petal.Width > 0.6
| Petal.Width <= 1.7
| | Petal.Length <= 4.9: versicolor (48.0/1.0)
| | Petal.Length > 4.9
| | | Petal.Width <= 1.5: virginica (3.0)
| | | Petal.Width > 1.5: versicolor (3.0/1.0)
| Petal.Width > 1.7: virginica (46.0/1.0)
Number of Leaves : 5
Size of the tree : 9
Logistic model tree
------------------
: LM_1:11/11 (14)
Number of Leaves : 1
Size of the Tree : 1
LM_1:
Class yes :
6.95 +
[outlook=sunny] * -0.65 +
[outlook=overcast] * 2.82 +
[temperature] * -0.02 +
[humidity] * -0.06 +
[windy=TRUE] * -1.38
Class no :
-6.95 +
[outlook=sunny] * 0.65 +
[outlook=overcast] * -2.82 +
[temperature] * 0.02 +
[humidity] * 0.06 +
[windy=TRUE] * 1.38
M5 pruned model tree:
(using smoothed linear models)
CHMIN <= 7.5 :
| MMAX <= 6100 : LM1 (76/7.046%)
| MMAX > 6100 :
| | CACH <= 28 :
| | | CACH <= 0.5 : LM2 (20/7.795%)
| | | CACH > 0.5 :
| | | | CHMIN <= 3.5 : LM3 (34/11.658%)
| | | | CHMIN > 3.5 : LM4 (9/12.866%)
| | CACH > 28 : LM5 (26/19.269%)
CHMIN > 7.5 :
| MMAX <= 28000 :
| | MMAX <= 13240 :
| | | CACH <= 81.5 : LM6 (6/18.551%)
| | | CACH > 81.5 : LM7 (4/30.824%)
| | MMAX > 13240 : LM8 (11/24.185%)
| MMAX > 28000 : LM9 (23/48.302%)
LM num: 1
class =
0.0025 * MMIN
+ 0.004 * MMAX
+ 0.0009 * CACH
+ 0.1758 * CHMIN
+ 0.205 * CHMAX
+ 7.6605
LM num: 2
class =
-0.012 * MYCT
- 0.0141 * MMIN
+ 0.0013 * MMAX
+ 0.0017 * CACH
+ 0.2793 * CHMIN
+ 0.6771 * CHMAX
+ 26.4438
LM num: 3
class =
-0.0096 * MMIN
+ 0.0028 * MMAX
+ 0.0018 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 24.6136
LM num: 4
class =
-0.0406 * MYCT
- 0.0096 * MMIN
+ 0.0033 * MMAX
- 0.294 * CACH
+ 0.2793 * CHMIN
+ 0.4086 * CHMAX
+ 38.0329
LM num: 5
class =
-0.0077 * MMIN
+ 0.0024 * MMAX
+ 0.3125 * CACH
+ 0.3866 * CHMIN
+ 0.5207 * CHMAX
+ 17.2559
LM num: 6
class =
-0.5146 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.3906 * CACH
- 2.0512 * CHMIN
+ 70.8672
LM num: 7
class =
-0.5896 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.4034 * CACH
- 2.0512 * CHMIN
+ 83.0016
LM num: 8
class =
-0.3653 * MYCT
- 0.516 * MMIN
+ 0.0086 * MMAX
+ 0.2587 * CACH
- 1.957 * CHMIN
+ 82.5725
LM num: 9
class =
-0.4748 * MMIN
+ 0.0083 * MMAX
+ 0.003 * CACH
- 0.9387 * CHMIN
+ 2.2489 * CHMAX
- 51.8474
Number of Rules : 9
outlook temperature humidity windy play
1 sunny 'All' 'All' FALSE no
2 sunny 'All' 'All' TRUE no
3 overcast 'All' 'All' FALSE yes
4 rainy 'All' 'All' FALSE yes
5 rainy 'All' 'All' FALSE yes
6 rainy 'All' 'All' TRUE no
7 overcast 'All' 'All' TRUE yes
8 sunny 'All' 'All' FALSE no
9 sunny 'All' 'All' FALSE yes
10 rainy 'All' 'All' FALSE yes
11 sunny 'All' 'All' TRUE yes
12 overcast 'All' 'All' TRUE yes
13 overcast 'All' 'All' FALSE yes
14 rainy 'All' 'All' TRUE no
outlook temperature humidity windy play
1 sunny 1.00000000 0.6451613 FALSE no
2 sunny 0.76190476 0.8064516 TRUE no
3 overcast 0.90476190 0.6774194 FALSE yes
4 rainy 0.28571429 1.0000000 FALSE yes
5 rainy 0.19047619 0.4838710 FALSE yes
6 rainy 0.04761905 0.1612903 TRUE no
7 overcast 0.00000000 0.0000000 TRUE yes
8 sunny 0.38095238 0.9677419 FALSE no
9 sunny 0.23809524 0.1612903 FALSE yes
10 rainy 0.52380952 0.4838710 FALSE yes
11 sunny 0.52380952 0.1612903 TRUE yes
12 overcast 0.38095238 0.8064516 TRUE yes
13 overcast 0.80952381 0.3225806 FALSE yes
14 rainy 0.33333333 0.8387097 TRUE no
outlook temperature humidity windy play
1 sunny 'All' 'All' FALSE no
2 sunny 'All' 'All' TRUE no
3 overcast 'All' 'All' FALSE yes
4 rainy 'All' 'All' FALSE yes
5 rainy 'All' 'All' FALSE yes
6 rainy 'All' 'All' TRUE no
7 overcast 'All' 'All' TRUE yes
8 sunny 'All' 'All' FALSE no
9 sunny 'All' 'All' FALSE yes
10 rainy 'All' 'All' FALSE yes
11 sunny 'All' 'All' TRUE yes
12 overcast 'All' 'All' TRUE yes
13 overcast 'All' 'All' FALSE yes
14 rainy 'All' 'All' TRUE no
outlook temperature humidity windy play
1 sunny 1.00000000 0.6451613 FALSE no
2 sunny 0.76190476 0.8064516 TRUE no
3 overcast 0.90476190 0.6774194 FALSE yes
4 rainy 0.28571429 1.0000000 FALSE yes
5 rainy 0.19047619 0.4838710 FALSE yes
6 rainy 0.04761905 0.1612903 TRUE no
7 overcast 0.00000000 0.0000000 TRUE yes
8 sunny 0.38095238 0.9677419 FALSE no
9 sunny 0.23809524 0.1612903 FALSE yes
10 rainy 0.52380952 0.4838710 FALSE yes
11 sunny 0.52380952 0.1612903 TRUE yes
12 overcast 0.38095238 0.8064516 TRUE yes
13 overcast 0.80952381 0.3225806 FALSE yes
14 rainy 0.33333333 0.8387097 TRUE no
outlook temperature humidity windy play
1 sunny 'All' 'All' FALSE no
2 sunny 'All' 'All' TRUE no
3 overcast 'All' 'All' FALSE yes
4 rainy 'All' 'All' FALSE yes
5 rainy 'All' 'All' FALSE yes
6 rainy 'All' 'All' TRUE no
7 overcast 'All' 'All' TRUE yes
8 sunny 'All' 'All' FALSE no
9 sunny 'All' 'All' FALSE yes
10 rainy 'All' 'All' FALSE yes
11 sunny 'All' 'All' TRUE yes
12 overcast 'All' 'All' TRUE yes
13 overcast 'All' 'All' FALSE yes
14 rainy 'All' 'All' TRUE no
outlook temperature humidity windy play
1 sunny 1.00000000 0.6451613 FALSE no
2 sunny 0.76190476 0.8064516 TRUE no
3 overcast 0.90476190 0.6774194 FALSE yes
4 rainy 0.28571429 1.0000000 FALSE yes
5 rainy 0.19047619 0.4838710 FALSE yes
6 rainy 0.04761905 0.1612903 TRUE no
7 overcast 0.00000000 0.0000000 TRUE yes
8 sunny 0.38095238 0.9677419 FALSE no
9 sunny 0.23809524 0.1612903 FALSE yes
10 rainy 0.52380952 0.4838710 FALSE yes
11 sunny 0.52380952 0.1612903 TRUE yes
12 overcast 0.38095238 0.8064516 TRUE yes
13 overcast 0.80952381 0.3225806 FALSE yes
14 rainy 0.33333333 0.8387097 TRUE no
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