Predict new data.
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 class '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)
p = predict(model, newdata = iris, subset = test.set)
print(p)
predict(model, task = iris.task, subset = test.set)
# predict now probabiliies instead of class labels
lrn = makeLearner("classif.lda", predict.type = "prob")
model = train(lrn, iris.task, subset = train.set)
p = predict(model, task = iris.task, subset = test.set)
print(p)
getPredictionProbabilities(p)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.