R:将游侠与插入符，tuneGrid参数一起使用 [英] R: using ranger with caret, tuneGrid argument

问题描述

我正在使用插入符包来分析随机使用 ranger 构建的森林模型.我不知道如何使用tuneGrid参数调优模型参数来调用训练函数.

I'm using the caret package to analyse Random Forest models built using ranger. I can't figure out how to call the train function using the tuneGrid argument to tune the model parameters.

我认为我把tuneGrid参数称为错误，但无法弄清楚为什么它是错误的.任何帮助将不胜感激.

I think I'm calling the tuneGrid argument wrong, but can't figure out why it's wrong. Any help would be appreciated.

data(iris)

library(ranger)
model_ranger <- ranger(Species ~ ., data = iris, num.trees = 500, mtry = 4,
                       importance = 'impurity')


library(caret)

# my tuneGrid object:
tgrid <- expand.grid(
  num.trees = c(200, 500, 1000),
  mtry = 2:4
)

model_caret <- train(Species  ~ ., data = iris,
                     method = "ranger",
                     trControl = trainControl(method="cv", number = 5, verboseIter = T, classProbs = T),
                     tuneGrid = tgrid,
                     importance = 'impurity'
)

推荐答案

以下是插入符中游侠的语法:

Here is the syntax for ranger in caret:

library(caret)

在调整参数之前添加.:

tgrid <- expand.grid(
  .mtry = 2:4,
  .splitrule = "gini",
  .min.node.size = c(10, 20)
)

插入号仅支持这三个，而不支持树的数量.在训练中，您可以指定数字树和重要性:

Only these three are supported by caret and not the number of trees. In train you can specify num.trees and importance:

model_caret <- train(Species  ~ ., data = iris,
                     method = "ranger",
                     trControl = trainControl(method="cv", number = 5, verboseIter = T, classProbs = T),
                     tuneGrid = tgrid,
                     num.trees = 100,
                     importance = "permutation")

获得可变的重要性:

varImp(model_caret)

#output
             Overall
Petal.Length 100.0000
Petal.Width   84.4298
Sepal.Length   0.9855
Sepal.Width    0.0000

要检查是否可以将树木数量设置为1000+，拟合速度会慢很多.更改importance = "impurity"之后:

To check if this works set number of trees to 1000+ - the fit will be much slower. After changing importance = "impurity":

#output:

             Overall
Petal.Length  100.00
Petal.Width    81.67
Sepal.Length   16.19
Sepal.Width     0.00

如果它不起作用，我建议从CRAN安装最新的游侠，并从git hub安装插入符号:

If it does not work I recommend installing latest ranger from CRAN and caret from git hub:

devtools::install_github('topepo/caret/pkg/caret')

要训练树的数量，可以将lapply与由createMultiFolds或createFolds创建的固定折叠一起使用.

To train the number of trees you can use lapply with fixed folds created by createMultiFolds or createFolds.

编辑:尽管以上示例适用于插入符号软件包6.0-84，但使用不带点的超级参数名称也适用.

EDIT: while the above example works with caret package version 6.0-84, using the names of hyper parameters without dots works as well.

tgrid <- expand.grid(
  mtry = 2:4,
  splitrule = "gini",
  min.node.size = c(10, 20)
)

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