旋风加速官网版
2024-07-14
旋风加速官网版
policy.mofcom.gov.cn:由于此网站的设置,我伔无法提供该页面的具体描述。
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.