Python Machine Learning Cookbook - Second Edition

How to do it...

Let's see how to extract confidence measurements:

1. The full code is given in the svm_confidence.py file, already provided to you. We will discuss the code of the recipe here. Let's define some input data:

import numpy as npimport matplotlib.pyplot as pltfrom sklearn.svm import SVCimport utilities# Load input datainput_file = 'data_multivar.txt'X, y = utilities.load_data(input_file)

2. At this point, we split the data for training and testing, and then we will build the classifier:

from sklearn import model_selectionX_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=5)params = {'kernel': 'rbf'}classifier = SVC(**params, gamma='auto')classifier.fit(X_train, y_train)

3. Define ...