Python for Finance Cookbook

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It is also common to split data into three sets: training, validation, and test. The validation set is used for frequent evaluation and tuning of the model's hyperparameters. Suppose we want to train a decision tree classifier and find the optimal value of the max_depth hyperparameter, which decides the maximum depth of the tree. To do so, we can train the model multiple times using the training set, and each time with a different value of the hyperparameter. Then, we can evaluate the performance of all these models, using the validation set. We pick the best model of those, and then, finally, evaluate its performance on the test set.

In the following code block, we illustrate a possible way of creating a train-validation-test ...