As a 30-second introductory example, we will train a decision tree model on the first 120 rows of iris data set and make predictions on the final 30, measuring the accuracy of the trained model.

library("mlr3")
task = tsk("iris")
learner = lrn("classif.rpart")

# train a model of this learner for a subset of the task
learner$train(task, row_ids = 1:120)
# this is what the decision tree looks like
learner$model

訓(xùn)練模型結(jié)果

predictions = learner$predict(task, row_ids = 121:150)
predictions

模型測(cè)試結(jié)果

predictions$score(msr("classif.acc"))
predictions$confusion
predictions$truth

測(cè)試結(jié)果參數(shù)