MOA_classifier: Create a MOA classifier

Description Usage Arguments Value See Also Examples

View source: R/Classification.R

Description

Create a MOA classifier

Usage

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MOA_classifier(model, control = NULL, ...)

Arguments

model

character string with a model. E.g. HoeffdingTree, DecisionStump, NaiveBayes, HoeffdingOptionTree, ... The list of known models can be obtained by typing RMOA:::.moaknownmodels. See the examples and MOAoptions.

control

an object of class MOAmodelOptions as obtained by calling MOAoptions

...

options of parameters passed on to MOAoptions, in case control is left to NULL. Ignored if control is supplied

Value

An object of class MOA_classifier

See Also

MOAoptions

Examples

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RMOA:::.moaknownmodels
ctrl <- MOAoptions(model = "HoeffdingTree", leafprediction = "MC", 
   removePoorAtts = TRUE, binarySplits = TRUE, tieThreshold = 0.20)
hdt <- MOA_classifier(model = "HoeffdingTree", control=ctrl)
hdt
hdt <- MOA_classifier(
 model = "HoeffdingTree", 
 numericEstimator = "GaussianNumericAttributeClassObserver")
hdt

Example output

Loading required package: RMOAjars
OpenJDK 64-Bit Server VM warning: Can't detect initial thread stack location - find_vma failed
Loading required package: rJava
 [1] "AdaHoeffdingOptionTree"        "ASHoeffdingTree"              
 [3] "DecisionStump"                 "HoeffdingAdaptiveTree"        
 [5] "HoeffdingOptionTree"           "HoeffdingTree"                
 [7] "LimAttHoeffdingTree"           "RandomHoeffdingTree"          
 [9] "NaiveBayes"                    "NaiveBayesMultinomial"        
[11] "ActiveClassifier"              "AccuracyUpdatedEnsemble"      
[13] "AccuracyWeightedEnsemble"      "ADACC"                        
[15] "DACC"                          "LeveragingBag"                
[17] "LimAttClassifier"              "OCBoost"                      
[19] "OnlineAccuracyUpdatedEnsemble" "OzaBag"                       
[21] "OzaBagAdwin"                   "OzaBagASHT"                   
[23] "OzaBoost"                      "OzaBoostAdwin"                
[25] "TemporallyAugmentedClassifier" "WeightedMajorityAlgorithm"    
[27] "AMRulesRegressor"              "FadingTargetMean"             
[29] "FIMTDD"                        "ORTO"                         
[31] "Perceptron"                    "SGD"                          
[33] "TargetMean"                   
HoeffdingTree modelling options: 
MOA model name: Hoeffding Tree or VFDT.
  - maxByteSize: 33554432   (Maximum memory consumed by the tree.)
  - numericEstimator: GaussianNumericAttributeClassObserver   (Numeric estimator to use.)
  - nominalEstimator: NominalAttributeClassObserver   (Nominal estimator to use.)
  - memoryEstimatePeriod: 1000000   (How many instances between memory consumption checks.)
  - gracePeriod: 200   (The number of instances a leaf should observe between split attempts.)
  - splitCriterion: InfoGainSplitCriterion   (Split criterion to use.)
  - splitConfidence: 1e-07   (The allowable error in split decision, values closer to 0 will take longer to decide.)
  - tieThreshold: 0.2   (Threshold below which a split will be forced to break ties.)
  - binarySplits: true   (Only allow binary splits.)
  - stopMemManagement: false   (Stop growing as soon as memory limit is hit.)
  - removePoorAtts: true   (Disable poor attributes.)
  - noPrePrune: false   (Disable pre-pruning.)
  - leafprediction: MC   (Leaf prediction to use.)
  - nbThreshold: 0   (The number of instances a leaf should observe before permitting Naive Bayes.)
Model type: moa.classifiers.trees.HoeffdingTree
model training instances = 0.0
model serialized size (bytes) = -1.0
tree size (nodes) = 0.0
tree size (leaves) = 0.0
active learning leaves = 0.0
tree depth = 0.0
active leaf byte size estimate = 0.0
inactive leaf byte size estimate = 0.0
byte size estimate overhead = 0.0
Model description:
Model has not been trained.HoeffdingTree modelling options: 
MOA model name: Hoeffding Tree or VFDT.
  - maxByteSize: 33554432   (Maximum memory consumed by the tree.)
  - numericEstimator: GaussianNumericAttributeClassObserver   (Numeric estimator to use.)
  - nominalEstimator: NominalAttributeClassObserver   (Nominal estimator to use.)
  - memoryEstimatePeriod: 1000000   (How many instances between memory consumption checks.)
  - gracePeriod: 200   (The number of instances a leaf should observe between split attempts.)
  - splitCriterion: InfoGainSplitCriterion   (Split criterion to use.)
  - splitConfidence: 1e-07   (The allowable error in split decision, values closer to 0 will take longer to decide.)
  - tieThreshold: 0.05   (Threshold below which a split will be forced to break ties.)
  - binarySplits: false   (Only allow binary splits.)
  - stopMemManagement: false   (Stop growing as soon as memory limit is hit.)
  - removePoorAtts: false   (Disable poor attributes.)
  - noPrePrune: false   (Disable pre-pruning.)
  - leafprediction: NBAdaptive   (Leaf prediction to use.)
  - nbThreshold: 0   (The number of instances a leaf should observe before permitting Naive Bayes.)
Model type: moa.classifiers.trees.HoeffdingTree
model training instances = 0.0
model serialized size (bytes) = -1.0
tree size (nodes) = 0.0
tree size (leaves) = 0.0
active learning leaves = 0.0
tree depth = 0.0
active leaf byte size estimate = 0.0
inactive leaf byte size estimate = 0.0
byte size estimate overhead = 0.0
Model description:
Model has not been trained.Warning message:
system call failed: Cannot allocate memory 

RMOA documentation built on Sept. 22, 2018, 5:04 p.m.