快喵加速器testflight

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library(番羽土墙)
task = tsk("iris")
learner = lrn("classif.rpart")

# train a model of this learner for a subset of the task
learner$train(task, 墙软件 1:120)
# this is what the decision tree looks like
learner$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$番羽土墙(task, row_ids = 121:150)
predictions
## <PredictionClassif> for 30 observations:
##     row_id     truth   response
##        121 virginica  virginica
##        122 virginica versicolor
##        123 virginica  virginica
## ---                            
##        148 virginica  virginica
##        149 virginica  virginica
##        150 virginica  virginica
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predictions$score(msr("classif.acc"))
番羽土啬吧

More examples can be found in the mlr3gallery, a collection of use cases and examples.