Hands-On Ensemble Learning with Python

Using random forests

Finally, we will employ a random forest ensemble. Once again, using validation curves, we will determine the optimal ensemble size. From the following graph, we conclude that 50 trees provide the least possible variance in our model, thus we proceed with ensemble size 50:

Validation curves for random forest

We provide the training and validation code as follows, as well as the achieved performance for both datasets. The following code is responsible for loading the required libraries and data, and training and evaluating the ensemble on the original and filtered datasets. We first load the required libraries and data, ...