We used random forests to build a classifier in the previous recipe, Evaluating cars based on their characteristics, but we don't exactly know how to define the parameters. In our case, we dealt with two parameters: n_estimators and max_depth. They are called hyperparameters, and the performance of the classifier depends on them. It would be nice to see how the performance gets affected as we change the hyperparameters. This is where validation curves come into the picture.
Extracting validation curves
Getting ready
Validation curves help us understand how each hyperparameter influences the training score. Basically, all other parameters are kept constant and we vary the hyperparameter of interest according to our range...
