mlr3 manual 
2019-12-09
Quickstart
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
## n= 120
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 120 70 setosa (0.41667 0.41667 0.16667)
## 2) Petal.Length< 2.45 50 0 setosa (1.00000 0.00000 0.00000) *
## 3) Petal.Length>=2.45 70 20 versicolor (0.00000 0.71429 0.28571)
## 6) Petal.Length< 4.95 49 1 versicolor (0.00000 0.97959 0.02041) *
## 7) Petal.Length>=4.95 21 2 virginica (0.00000 0.09524 0.90476) *
predictions = learner$predict(task, row_ids = 121:150)
predictions
## <PredictionClassif> for 30 observations:
## row_id truth response
## 121 virginica virginica
## 122 virginica versicolor
## 123 virginica virginica
## ---
## 148 virginica virginica
## 149 virginica virginica
## 150 virginica virginica
# accuracy of our model on the test set of the final 30 rows
predictions$score(msr("classif.acc"))
## classif.acc
## 0.8333The code examples in this book use a few additional packages that are not installed by default if you install mlr3.
To run all examples in the book, install the mlr3book package using the remotes package: