Python Machine Learning Cookbook - Second Edition

How to do it...

Let's see how to find optimal hyperparameters:

1. The full code is given in the perform_grid_search.py file that's already provided to you. We start importing the libraries:

from sklearn import svmfrom sklearn import model_selectionfrom sklearn.model_selection import GridSearchCVfrom sklearn.metrics import classification_reportimport pandas as pdimport utilities 

2. Then, we load the data:

input_file = 'data_multivar.txt'X, y = utilities.load_data(input_file)

3. We split the data into a train and test dataset:

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=5)

4. Now, we will use cross-validation here, which we covered in the previous recipes. Once you load the data and split ...