Prediction using Random Forest in R

Question

I created a model in R for my df as

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

gives result as

 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

but while I am predicting using

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

Shows error

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

When I am using

predict(fit, newdata= data_test)

Showing error

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

how can I solve this.. I am new in using RandomForest in R

Why do you have keep.forest = FALSE? Try changing that to TRUE and re-attempt your second prediction approach. — bzki
– bzki, Commented Sep 10, 2022 at 2:04

zainul abid · Accepted Answer · 2022-09-10 04:29:50Z

1

As @bzki suggested By putting keep.forest = TRUE It works fine

answered Sep 10, 2022 at 4:29

zainul abid

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