As discussed in the previous chapter, hyperparameters are important for determining the performance of a classifier. Let's see how to extract optimal hyperparameters for SVMs.
Finding optimal hyperparameters
Getting ready
In machine learning algorithms, various parameters are obtained during the learning process. In contrast, hyperparameters are set before the learning process begins. Given these hyperparameters, the training algorithm learns the parameters from the data. In this recipe, we will extract hyperparameters for a model based on an SVM algorithm using the grid search method.
