Choosing the Cut-off (Threshold) Value for Selecting the Important Features in a Random Forest
The right way to drop the least important features in a random forest model
Random forest algorithms play an important role in machine learning as they work well with non-linear data which is common in real-world scenarios.
If you have read this article, you already know that all features in the dataset do not contribute the same to the random forest model. It means that we should remove the least important features to reduce model complexity and remove the noise in the data.
I have already discussed the standard procedure for selecting the most important features in a random forest in this article. But, here I want to add some extra points regarding choosing the cut-off (threshold) value that drops the least important features from the model.
First of all, I want to emphasize that choosing the cut-off value depends on the type of the dataset, the task and the user who wants to achieve it.
We should consider maintaining the balance between model complexity and performance. If we drop a lot of features, the model performance will get even worse. If we keep many features, the model will be too complex.
