随机森林 - mtry 如何大于自变量总数？

Question

1) 我尝试使用回归随机森林来训练 185 行的数据集，其中包含 4 个自变量。 2个分类变量各有3个水平，13个水平。另外 2 个变量是数字连续变量。

我尝试使用 RF 进行 10 次交叉验证，重复 4 次。（我没有缩放因变量，这就是 RMSE 如此之大的原因。）

我猜 mtry 大于 4 的原因是分类变量总共有 3+13= 16 个水平。但如果是这样，为什么它不包括数字变量 number?

185 samples
4 predictor

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 4 times) 
Summary of sample sizes: 168, 165, 166, 167, 166, 167, ... 
Resampling results across tuning parameters:

  mtry  RMSE      Rsquared   MAE    
   2    16764183  0.7843863  9267902
   9     9451598  0.8615202  3977457
  16     9639984  0.8586409  3813891

RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 9.

请帮助我理解mtry。

2）另外，每折样本量是168,165,166，....，为什么样本量在变化？

sample sizes: 168, 165, 166, 167, 166, 167

非常感谢。

Answer 1

您是正确的，因为有 16 个变量可供采样，因此 mtry 的最大值为 16。

caret选择的值基于两个参数，在train中，tuneLength有一个选项，默认为3:

tuneLength = ifelse(trControl$method == "none", 1, 3)

这意味着它测试三个值。对于randomForest，你有mtry，默认是：

getModelInfo("rf")[[1]]$grid
function(x, y, len = NULL, search = "grid"){
                    if(search == "grid") {
                      out <- data.frame(mtry = caret::var_seq(p = ncol(x), 
                                                              classification = is.factor(y), 
                                                              len = len))
                    } else {
                      out <- data.frame(mtry = unique(sample(1:ncol(x), size = len, replace = TRUE)))
                    }
                    out
                  }

因为你有 16 列，它变成：

var_seq(16,len=3)
[1]  2  9 16

您可以通过设置来测试您选择的mtry：

library(caret)
trCtrl = trainControl(method="repeatedcv",repeats=4,number=10)
# we test 2,4,6..16
trg = data.frame(mtry=seq(2,16,by=2))
# some random data for example
df = data.frame(y=rnorm(200),x1 = sample(letters[1:13],200,replace=TRUE),
x2=sample(LETTERS[1:3],200,replace=TRUE),x3=rpois(200,10),x4=runif(200))

#fit
mdl = train(y ~.,data=df,tuneGrid=trg,trControl =trCtrl)

Random Forest 

200 samples
  4 predictor

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 4 times) 
Summary of sample sizes: 180, 180, 180, 180, 180, 180, ... 
Resampling results across tuning parameters:

  mtry  RMSE      Rsquared    MAE      
   2    1.120216  0.04448700  0.8978851
   4    1.157185  0.04424401  0.9275939
   6    1.172316  0.04902991  0.9371778
   8    1.186861  0.05276752  0.9485516
  10    1.193595  0.05490291  0.9543479
  12    1.200837  0.05608624  0.9574420
  14    1.205663  0.05374614  0.9621094
  16    1.210783  0.05537412  0.9665665

RMSE was used to select the optimal model using the smallest value.
The final value used for the model was mtry = 2.

随机森林 - mtry 如何大于自变量总数？

Random Forest - how mtry is larger than total number of independent variable?

r

random-forest

r-caret

interpretation