在 R 中使用隨機森林進行預測

Question

我在 R 中為我的 df 創建了一個 model 作為

fit <- randomForest(y ~ x1 + 
                     x2 + x3 + 
                     x4+ x5+x6+ x7+x8, 
                     data = data_train,ntree=35,
                     keep.forest=FALSE, importance=TRUE)

結果為

 Call:
  randomForest(formula = y ~ x1 + x2 
 +      x3 + x4 + x5 + 
   x6 +      x7 + x8, data = 
data_train, ntree = 35,      keep.forest = FALSE, importance = TRUE) 
           Type of random forest: regression
                 Number of trees: 35
   No. of variables tried at each split: 2

        Mean of squared residuals: 2901510
                % Var explained: 53.45

但是當我預測使用

p <- predict(data_test, fit, type='prob')

顯示錯誤

Error in ets(object, lambda = lambda, biasadj = biasadj, 
allow.multiplicative.trend = allow.multiplicative.trend,  : 
 y should be a univariate time series

當我使用

predict(fit, newdata= data_test)

顯示錯誤

Error in predict.randomForest(fit, newdata = data_test) : 
 No forest component in the object

我該如何解決這個問題。我是在 R 中使用 RandomForest 的新手

Answer 1

正如@bzki 所建議的那樣通過把keep.forest = TRUE 它工作正常