Predict the target variable of new data using a fitted model.
What is stored exactly in the (Prediction) object depends
on the predict.type
setting of the Learner.
If predict.type
was set to “prob” probability thresholding
can be done calling the setThreshold function on the
prediction object.
The row names of the input task
or newdata
are preserved in the output.
# S3 method for WrappedModel predict(object, task, newdata, subset = NULL, ...)
object | (WrappedModel) |
---|---|
task | (Task) |
newdata | (data.frame) |
subset | (integer | logical | |
... | (any) |
(Prediction).
Other predict:
asROCRPrediction()
,
getPredictionProbabilities()
,
getPredictionResponse()
,
getPredictionTaskDesc()
,
setPredictThreshold()
,
setPredictType()
# train and predict train.set = seq(1, 150, 2) test.set = seq(2, 150, 2) model = train("classif.lda", iris.task, subset = train.set)#> Error: Please use column names for `x`#> Error in predict(model, newdata = iris, subset = test.set): object 'model' not found#> Error in print(p): object 'p' not found#> Error in predict(model, task = iris.task, subset = test.set): object 'model' not found# predict now probabiliies instead of class labels lrn = makeLearner("classif.lda", predict.type = "prob") model = train(lrn, iris.task, subset = train.set)#> Error: Please use column names for `x`#> Error in predict(model, task = iris.task, subset = test.set): object 'model' not found#> Error in print(p): object 'p' not found#> Error in checkClass(x, classes, ordered, null.ok): object 'p' not found