Train a learning algorithm.
Given a Task, creates a model for the learning machine which can be used for predictions on new data.
train(learner, task, subset = NULL, weights = NULL)
learner |
(Learner | |
task |
(Task) |
subset |
(integer | logical | |
weights |
(numeric) |
(WrappedModel).
training.set = sample(seq_len(nrow(iris)), nrow(iris) / 2) ## use linear discriminant analysis to classify iris data task = makeClassifTask(data = iris, target = "Species") learner = makeLearner("classif.lda", method = "mle") mod = train(learner, task, subset = training.set) print(mod) ## use random forest to classify iris data task = makeClassifTask(data = iris, target = "Species") learner = makeLearner("classif.rpart", minsplit = 7, predict.type = "prob") mod = train(learner, task, subset = training.set) print(mod)
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