Till now I have used a following flow for training a random forest model.
create 10 folds of data. for each fold i: - use ith fold as validation data - use remaining 9 folds as training data - apply normalization on training and validation data - # apply feature selection on training data - # select same features from validation data - train random forest on training data - predict values for validation data combine all predictions.
Now I want to do feature selection using
varImp() function. I am confused as it is said that
varImp itself trains a model on training data to find out best set of features.
How should I use
varImp to get important features (say using
partial least squares) and then again apply training model on training data?