Problems with my code and lime package in R

Asked May 05 '20 at 15:56

Active May 07 '20 at 08:15

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I'm trying to use the "lime" package to interpret a Random Forest model with the "import85" dataset, but when I run the explain command it returns an error:

library(lime)
library(caret)

data("imports85", package = "randomForest")

imp85 <- imports85[,-2] 
imp85 <- imp85[complete.cases(imp85), ] 
imp85[] <- lapply(imp85, function(x) if (is.factor(x)) x[, drop=TRUE] else x)
stopifnot(require(randomForest)) 

NROW(imp85)*0.7
idx <- sample(1:NROW(imp85),135)
test <- imp85[idx, c(1:4, 6:25)]
train <- imp85[-idx, c(1:4, 6:25)]
resp <- imp85[[5]][-idx]

model <- train(train, resp, method = 'rf')
explainer <- lime(train, model)

explanation <- explain(test, explainer, n_labels = 1, n_features = 2)

Error in predict.randomForest(modelFit, newdata, type = "prob") :
Type of predictors in new data do not match that of the training data.

How can I solve it?

EDIT 1: I tried to force the factor variable levels to be the same for both train and test datasets, but it doesn't work

edited May 07 '20 at 08:15

asked May 05 '20 at 15:56

GN_Agostino

check for the class of both datasets, you can use lapply(train,class) and lapply(test, class) then check for class – rj-nirbhay May 05 '20 at 15:58
thanks for the answer, I have already checked but the classes are the same for both – GN_Agostino May 05 '20 at 16:04

Problems with my code and lime package in R

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