R randomForest 子集无法摆脱因子水平 [英] R randomForest subsetting can't get rid of factor levels
问题描述
可能的重复:
在 R 中的子集数据框中删除因子级别
我正在尝试使用 randomForest 来预测销售额.我有 3 个变量，其中一个是 storeId 的因子变量.我知道测试集中的某些级别不在训练集中.我正在尝试仅对训练集中存在的级别进行预测，但无法使其超越新的因子级别.
I'm trying to use a randomForest to predict sales. I have 3 variables, one of which is a factor variable for storeId. I know that there are levels in the test set that are NOT in the training set. I'm trying to get a prediction for only levels present in the training set but can't get it to look past the new factor levels.
这是我迄今为止尝试过的:
Here's what I've tried so far:
require(randomForest)
train <- data.frame(sales = runif(10)*1000, storeId = factor(seq(1,10,1)), dat1 =runif(10), dat2 = runif(10)*10)
test <- data.frame(storeId = factor(seq(2,11,1)), dat1 =runif(10), dat2 = runif(10)*10)
> train
sales storeId dat1 dat2
1 414.7791 1 0.7830092 7.178577
2 719.5965 2 0.9512138 6.153049
3 887.3197 3 0.6879827 5.413556
4 706.5828 4 0.4486214 4.955400
5 326.8189 5 0.0944885 6.900802
6 840.5920 6 0.1917165 8.044636
7 936.2206 7 0.2173074 4.835064
8 244.6947 8 0.6526765 6.516790
9 818.8747 9 0.3317644 9.651675
10 631.6104 10 0.6998037 8.443972
> test
storeId dat1 dat2
1 2 0.7513645 3.442052
2 3 0.2862487 3.196189
3 4 0.4971865 6.074281
4 5 0.8631945 8.766129
5 6 0.3848105 5.001426
6 7 0.9032262 7.018274
7 8 0.1560501 4.523618
8 9 0.3461597 5.551672
9 10 0.1318464 3.092640
10 11 0.6587270 1.348623
> RF1 <- randomForest(train[,c("storeId","dat1","dat2")], train$sales, do.trace=TRUE,
+ importance=TRUE,ntree=5,,forest=TRUE)
| Out-of-bag |
Tree | MSE %Var(y) |
1 | 2.915e+05 544.44 |
2 | 1.825e+05 340.84 |
3 | 2.1e+05 392.19 |
4 | 1.914e+05 357.38 |
5 | 1.809e+05 337.78 |
> pred <- predict(RF1, test)
Error in predict.randomForest(RF1, test) :
New factor levels not present in the training data
这部分很有意义.
所以我试试这个:
> test2 <- test[test$storeId != 11,]
> pred <- predict(RF1, test2)
Error in predict.randomForest(RF1, test2) :
New factor levels not present in the training data
所以我试试这个:
> levels(test2$storeId)
[1] "2" "3" "4" "5" "6" "7" "8" "9" "10" "11"
11"级别还在那里.
接下来我试试这个:
> test2$storeId <- as.numeric(as.character(test2$storeId))
> test2$storeId <- factor(test2$storeId)
> pred <- predict(RF1, test2)
Error in predict.randomForest(RF1, test2) :
Type of predictors in new data do not match that of the training data.
尽管这里看起来一切正常:
despite the fact that things look ok here:
> levels(test2$storeId)
[1] "2" "3" "4" "5" "6" "7" "8" "9" "10"
有什么建议可以让它在没有11"级别的商店中进行预测?
Any suggestions for getting it to predict on just stores without the "11" level?
> test2$storeId <- as.factor(as.character(test2$storeId))
> pred <- predict(RF1, test2)
Error in predict.randomForest(RF1, test2) :
Type of predictors in new data do not match that of the training data.
>
> test2$storeId <- drop.levels(test2$storeId)
> pred <- predict(RF1, test2)
Error in predict.randomForest(RF1, test2) :
Type of predictors in new data do not match that of the training data.
> str(train)
'data.frame': 10 obs. of 4 variables:
$ sales : num 800 679 589 812 384 ...
$ storeId: Factor w/ 10 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10
$ dat1 : num 0.5148 0.5567 0.9871 0.0071 0.736 ...
$ dat2 : num 8.501 2.994 2.948 0.519 1.746 ...
> str(test)
'data.frame': 10 obs. of 3 variables:
$ storeId: Factor w/ 10 levels "2","3","4","5",..: 1 2 3 4 5 6 7 8 9 10
$ dat1 : num 0.0975 0.7435 0.7055 0.2085 0.2944 ...
$ dat2 : num 5.96 6.84 3.96 8.93 8.62 ...
> str(test2)
'data.frame': 9 obs. of 3 variables:
$ storeId: Factor w/ 9 levels "2","3","4","5",..: 1 2 3 4 5 6 7 8 9
$ dat1 : num 0.0975 0.7435 0.7055 0.2085 0.2944 ...
$ dat2 : num 5.96 6.84 3.96 8.93 8.62 ...
推荐答案
与 rf 模型相比，您无法对具有缺失因子的新数据运行 randomForest 预测函数.由于 test$storeId 的因子级别范围为 "2"-"11" 和 train$storeId "1"-"10"，当您在测试数据中降低级别 11 时，您仍然缺少级别1"，因此 randomForest 预测失败了.
You cannot run the randomForest predict function on newdata that has missing factors as compared to the rf model. Since the factor levels of test$storeId range "2"-"11" and the train$storeId "1"-"10", when you drop level 11 in the test data your are still missing level "1" and thus randomForest predict is failing.
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