我正在使用R编程语言。我试图将“随机森林”（一种统计模型）拟合到我的数据中，但问题在于：我的一个分类变量拥有超过53个类别——显然，R中的“随机森林”包不允许用户有超过53个类别，这阻止了我使用这个变量进行建模。理想情况下，我希望能使用这个变量。
为了说明这个例子，我创建了一个数据集（称为“data”），其中一个变量拥有超过53个类别：
#load librarieslibrary(caret)library(randomforest)library(ranger)#first data setcat_var <- c("a","b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "aa", "bb", "cc", "dd", "ee", "ff", "gg", "hh", "ii", "jj", "kk", "ll", "mm", "nn", "oo", "pp", "qq", "rr", "ss", "tt", "uu", "vv", "ww", "xx", "yy", "zz", "aaa", "bbb")var_1 <- rnorm(54,10,10)var_2 <- rnorm(54, 5, 5)var_3 <- rnorm(54, 6,18)response <- c("a","b")response <- sample(response, 54, replace=TRUE, prob=c(0.3, 0.7))data_1 = data.frame(cat_var, var_1, var_2, var_3, response)data_1$response = as.factor(data_1$response)data_1$cat_var = as.factor(data_1$cat_var)#second data setcat_var <- c("a","b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "aa", "bb", "cc", "dd", "ee", "ff", "gg", "hh", "ii", "jj", "kk", "ll", "mm", "nn", "oo", "pp", "qq", "rr", "ss", "tt", "uu", "vv", "ww", "xx", "yy", "zz", "aaa", "bbb")var_1 <- rnorm(54,10,10)var_2 <- rnorm(54, 5, 5)var_3 <- rnorm(54, 6,18)response <- c("a","b")response <- sample(response, 54, replace=TRUE, prob=c(0.3, 0.7))data_2 = data.frame(cat_var, var_1, var_2, var_3, response)data_2$response = as.factor(data_2$response)data_2$cat_var = as.factor(data_2$cat_var)# third data setcat_var <- c("a","b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "aa", "bb", "cc", "dd", "ee", "ff", "gg", "hh", "ii", "jj", "kk", "ll", "mm", "nn", "oo", "pp", "qq", "rr", "ss", "tt", "uu", "vv", "ww", "xx", "yy", "zz", "aaa", "bbb")var_1 <- rnorm(54,10,10)var_2 <- rnorm(54, 5, 5)var_3 <- rnorm(54, 6,18)response <- c("a","b")response <- sample(response, 54, replace=TRUE, prob=c(0.3, 0.7))data_3 = data.frame(cat_var, var_1, var_2, var_3, response)data_3$response = as.factor(data_3$response)data_3$cat_var = as.factor(data_3$cat_var)#combine data setsdata = rbind(data_1, data_2, data_3)从这里开始，我对拟合随机森林模型感兴趣。我查看了不同的stackoverflow帖子（例如 R randomForest too many categories error even with fewer than 53 categories , R – Random Forest and more than 53 categories），以下是我注意到的情况。
如果你尝试直接拟合随机森林模型，会发生以下情况：
#random forest using the "Randomforest" libraryrf = randomForest(response ~ var_1 + var_2 + var_3 + cat_var, data=data, ntree=50, mtry=2)Error in randomForest.default(m, y, ...) : Can not handle categorical predictors with more than 53 categories.在其中一个帖子中，一位用户建议使用“caret”库来拟合模型——显然，caret模型没有53个类别的限制。这确实有效，但我不能确定这是否正确：
#random forest using the "caret" and "ranger" libraries: (are these correct?)random_forest <- train(response ~., data = data, method = 'ranger')random_forest <- train(response ~., data = data, method = 'rf')最后，另一位用户建议使用“模型矩阵”方法，但我不能确定我是否完全理解了这种方法：
#model matrix methoddummyMat <- model.matrix(response ~ var_1 + var_2 + var_3 + cat_var, data, # set contrasts.arg to keep all levels contrasts.arg = list(var_1 = contrasts(data$var_1, contrasts = T), var_3 = contrasts(data$var_3, contrasts = T), cat_var = contrasts(data$cat_var, contrasts = F) var_2 = contrasts(data$var2, contrasts = T))) data2 <- cbind(data, dummyMat[,c(4:ncol(dummyMat)]) # just removing intercept columnrf = randomForest(response ~ var_1 + var_2 + var_3 + cat_var, data=data2, ntree=50, mtry=2)请问有人能建议我如何解决这个问题吗？第二个方法（使用“caret”）是否正确？
谢谢
回答：
我可以告诉你，caret方法是正确的。caret包含了数据分割、预处理、特征选择和使用重采样交叉验证的模型调优工具。这里我发布了一个使用caret包拟合模型的典型工作流程（以你发布的数据为例）。
首先，我们为所选模型的超参数调优设置一种交叉验证方法（在你的情况下，调优参数是mtry，对于ranger和randomForest，splitrule和min.node.size对于ranger）。在示例中，我选择了k折交叉验证，k=10
library(caret)control <- trainControl(method="cv",number = 10)然后我们创建一个包含待调参数可能值的网格
rangergrid <- expand.grid(mtry=2:(ncol(data)-1),splitrule="extratrees",min.node.size=seq(0.1,1,0.1))rfgrid <- expand.grid(mtry=2:(ncol(data)-1))最后，我们拟合所选的模型：
random_forest_ranger <- train(response ~., data = data, method = 'ranger', trControl=control, tuneGrid=rangergrid)random_forest_rf <- train(response ~., data = data, method = 'rf', trControl=control, tuneGrid=rfgrid)train函数的输出看起来像这样：
> random_forest_rfRandom Forest 162 samples 4 predictor 2 classes: 'a', 'b' No pre-processingResampling: Cross-Validated (10 fold) Summary of sample sizes: 146, 146, 146, 145, 146, 146, ... Resampling results across tuning parameters: mtry Accuracy Kappa 2 0.6852941 0.00000000 3 0.6852941 0.00000000 4 0.6602941 -0.04499494Accuracy was used to select the optimal model using the largest value.The final value used for the model was mtry = 2.关于caret包的更多信息，请查看在线的指南
