Random Forest pruning
You could try ensemble pruning. This boils down to removing from your random forest a number of the decision trees that make it up.
If you remove trees at random, the expected outcome is that the performance of the ensemble will gradually deteriorate with the number of removed trees. However, you can do something more clever like removing those trees whose predictions are highly correlated with the predictions of the rest of the ensemble, and thus do to significantly modify the outcome of the whole ensemble.
Alternatively, you can train a linear classifier that uses as inputs the outputs of the individual ensembles, and include some kind of l1 penalty in the training to enforce sparse weights on the classifier. The weights with 0 or very small value will hint which trees could be removed from the ensemble with a small impact on accuracy.
I have sklearn random forest regressor. It's very heavy, 1.6 GBytes, and works very long time when predicting values.
I want to prune it to make lighter. As I know pruning is not implemented for decision trees and forests. I can't implement it by myself since tree code is written on C and I don't know it.
Does anyone know the solution?