Machine Learning with scikit-learn Quick Start Guide

Hyperparameter optimization using GridSearchCV

In this section, you will learn how to optimize the inverse regularization strength using the GridSearchCV algorithm. We can do this using the following code:

from sklearn.model_selection import GridSearchCV#Building the model svm = LinearSVC(random_state = 50)#Using GridSearchCV to search for the best parametergrid = GridSearchCV(svm, {'C':[0.00001, 0.0001, 0.001, 0.01, 0.1, 10]})grid.fit(X_train, y_train)# Print out the best parameterprint("The best value of the inverse regularization strength is:", grid.best_params_)

In the preceding code, the following applies:

1. First, we import the GridSearchCV module from scikit-learn
2. The next step is to initialize a linear support vector machine model ...