predict.OneR: Predict method for OneR models

In OneR: One Rule Machine Learning Classification Algorithm with Enhancements

Description

Predict cases or probabilities based on OneR model object.

Usage

## S3 method for class 'OneR'
predict(object, newdata, type = c("class", "prob"), ...)

Arguments

object	object of class "OneR".
newdata	data frame in which to look for the feature variable with which to predict.
type	character string denoting the type of predicted value returned. Default "class" gives a named vector with the predicted classes, "prob" gives a matrix whose columns are the probability of the first, second, etc. class.
...	further arguments passed to or from other methods.

Details

newdata can have the same format as used for building the model but must at least have the feature variable that is used in the OneR rules. If cases appear that were not present when building the model the predicted case is UNSEEN or NA when "type = prob".

Value

The default is a factor with the predicted classes, if "type = prob" a matrix is returned whose columns are the probability of the first, second, etc. class.

Author(s)

Holger von Jouanne-Diedrich

References

https://github.com/vonjd/OneR

See Also

OneR

Examples

model <- OneR(iris)
prediction <- predict(model, iris[1:4])
eval_model(prediction, iris[5])

## type prob
predict(model, data.frame(Petal.Width = seq(0, 3, 0.5)))
predict(model, data.frame(Petal.Width = seq(0, 3, 0.5)), type = "prob")

Example output

Confusion matrix (absolute):
            Actual
Prediction   setosa versicolor virginica Sum
  setosa         49          0         0  49
  versicolor      1         45         3  49
  virginica       0          5        47  52
  Sum            50         50        50 150

Confusion matrix (relative):
            Actual
Prediction   setosa versicolor virginica  Sum
  setosa       0.33       0.00      0.00 0.33
  versicolor   0.01       0.30      0.02 0.33
  virginica    0.00       0.03      0.31 0.35
  Sum          0.33       0.33      0.33 1.00

Accuracy:
0.94 (141/150)

Error rate:
0.06 (9/150)

Error rate reduction (vs. base rate):
0.91 (p-value < 2.2e-16)

(-Inf,0.0976] (0.0976,0.58]   (0.58,1.06]   (1.06,1.54]   (1.54,2.02] 
       UNSEEN        setosa    versicolor    versicolor     virginica 
   (2.02,2.5]    (2.5, Inf] 
    virginica        UNSEEN 
Levels: UNSEEN setosa versicolor virginica
              setosa versicolor  virginica
(-Inf,0.0976]     NA         NA         NA
(0.0976,0.58]  1.000  0.0000000 0.00000000
(0.58,1.06]    0.125  0.8750000 0.00000000
(1.06,1.54]    0.000  0.9268293 0.07317073
(1.54,2.02]    0.000  0.1724138 0.82758621
(2.02,2.5]     0.000  0.0000000 1.00000000
(2.5, Inf]        NA         NA         NA

OneR documentation built on May 2, 2019, 9:33 a.m.