Python: Real World Machine Learning

Finding optimal hyperparameters

As discussed in the previous chapter, hyperparameters are important in determining the performance of a classifier. Let's see how to extract optimal hyperparameters for SVMs.

How to do it…

1. The full code is given in the perform_grid_search.py file that's already provided to you. We will only discuss the core parts of the recipe here. We will use cross-validation here, which we covered in the previous recipes. Once you load the data and split it into training and testing datasets, add the following to the file:

   # Set the parameters by cross-validation parameter_grid = [ {'kernel': ['linear'], 'C': [1, 10, 50, 600]}, {'kernel': ['poly'], 'degree': [2, 3]}, {'kernel': ['rbf'], 'gamma': [0.01, 0.001], 'C': [1, 10, 50, 600]}, ...