Python for Finance Cookbook

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In this recipe, we showed how to use advanced classifiers to achieve better results. To make things even more interesting, these models have multiple hyperparameters to tune, which can significantly increase/decrease their performance.

For brevity, we do not discuss hyperparameter tuning of these models here. We refer to the Notebook in the GitHub repository for a short introduction to tuning these models using randomized grid search. Here, we only present the results, comparing the performance of the models with default settings versus their tuned counterparts.

Before describing the results, we briefly go over the details of the considered classifiers:

Random Forest: Random Forest is the first of the models we consider in ...