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
训练模型结果
predictions = learner$predict(task, row_ids = 121:150)
predictions
模型测试结果
predictions$score(msr("classif.acc"))
predictions$confusion
predictions$truth
测试结果参数
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