[英]Error with caret package - classification v regression
我是一名精算專業的學生,正在為即將在 12 月舉行的預測分析考試做准備。 練習的一部分是使用帶有caret
和xgbTree
boosting 來構建模型。 看下面的代碼,caravan數據集來自ISLR
包:
library(caret)
library(ggplot2)
set.seed(1000)
data.Caravan <- read.csv(file = "Caravan.csv")
data.Caravan$Purchase <- factor(data.Caravan$Purchase)
levels(data.Caravan$Purchase) <- c("No", "Yes")
data.Caravan.train <- data.Caravan[1:1000, ]
data.Caravan.test <- data.Caravan[1001:nrow(data.Caravan), ]
grid <- expand.grid(max_depth = c(1:7),
nrounds = 500,
eta = c(.01, .05, .01),
colsample_bytree = c(.5, .8),
gamma = 0,
min_child_weight = 1,
subsample = .6)
control <- trainControl(method = "cv",
number = 4,
classProbs = TRUE,
sampling = c("up", "down"))
caravan.boost <- train(formula = Purchase ~ .,
data = data.Caravan.train,
method = "xgbTree",
metric = "Accuracy",
trControl = control,
tuneGrid = grid)
expand.grid
和trainControl
中的定義由問題指定,但我不斷收到錯誤消息:
錯誤:采樣方法只針對分類問題實現
如果我從trainControl
刪除采樣方法,我會收到一個新錯誤,指出“度量精度不適用於回歸模型”。 如果我刪除了准確度指標,我會收到一條錯誤消息,指出
無法計算回歸的類概率”和“名稱錯誤(res$trainingData)%in% as.character(form[[2]]):缺少參數“form”,沒有默認值”
最終的問題是,插入符號將問題定義為回歸,而不是分類,即使目標變量設置為因子變量並且classProbs
設置為 TRUE。 有人可以解釋如何告訴插入符號運行分類而不是回歸嗎?
caret::train
沒有formula
參數,而是一個用於指定公式的form
參數。 因此,例如這有效:
caravan.boost <- train(form = Purchase ~ .,
data = data.Caravan.train,
method = "xgbTree",
metric = "Accuracy",
trControl = control,
tuneGrid = grid)
#output:
eXtreme Gradient Boosting
1000 samples
85 predictor
2 classes: 'No', 'Yes'
No pre-processing
Resampling: Cross-Validated (4 fold)
Summary of sample sizes: 751, 749, 750, 750
Addtional sampling using up-sampling
Resampling results across tuning parameters:
eta max_depth colsample_bytree Accuracy Kappa
0.01 1 0.5 0.7020495 0.10170007
0.01 1 0.8 0.7100335 0.09732773
0.01 2 0.5 0.7730581 0.12361444
0.01 2 0.8 0.7690620 0.11293561
0.01 3 0.5 0.8330506 0.14461709
0.01 3 0.8 0.8290146 0.06908344
0.01 4 0.5 0.8659949 0.07396586
0.01 4 0.8 0.8749790 0.07451637
0.01 5 0.5 0.8949792 0.07599005
0.01 5 0.8 0.8949792 0.07525191
0.01 6 0.5 0.9079873 0.09766492
0.01 6 0.8 0.9099793 0.10420720
0.01 7 0.5 0.9169833 0.11769151
0.01 7 0.8 0.9119753 0.10873268
0.05 1 0.5 0.7640699 0.08281792
0.05 1 0.8 0.7700580 0.09201503
0.05 2 0.5 0.8709909 0.09034807
0.05 2 0.8 0.8739990 0.10440898
0.05 3 0.5 0.9039792 0.12166348
0.05 3 0.8 0.9089832 0.11850402
0.05 4 0.5 0.9149793 0.11602447
0.05 4 0.8 0.9119713 0.11207786
0.05 5 0.5 0.9139633 0.11853793
0.05 5 0.8 0.9159754 0.11968085
0.05 6 0.5 0.9219794 0.11744643
0.05 6 0.8 0.9199794 0.12803204
0.05 7 0.5 0.9179873 0.08701058
0.05 7 0.8 0.9179793 0.10702619
Tuning parameter 'nrounds' was held constant at a value of 500
Tuning parameter 'gamma' was held constant
at a value of 0
Tuning parameter 'min_child_weight' was held constant at a value of 1
Tuning
parameter 'subsample' was held constant at a value of 0.6
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were nrounds = 500, max_depth = 6, eta = 0.05, gamma =
0, colsample_bytree = 0.5, min_child_weight = 1 and subsample = 0.6.
您還可以使用非公式接口,在其中分別指定x
和y
:
caravan.boost <- train(x = data.Caravan.train[,-ncol(data.Caravan.train)],
y = data.Caravan.train$Purchase,
method = "xgbTree",
metric = "Accuracy",
trControl = control,
tuneGrid = grid)
請注意,當x
有因子變量時,這兩種規范方式並不總是產生相同的結果,因為對於大多數算法,公式接口調用model.matrix
。
要獲取數據:
library(ISLR)
data(Caravan)
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