Machine Learning for OpenCV 4 - Second Edition

Implementing a voting classifier

Let's look at a simple example of a voting classifier that combines three different algorithms:

• A logistic regression classifier from Chapter 3, First Steps in Supervised Learning
• A Gaussian Naive Bayes classifier from Chapter 7, Implementing a Spam Filter with Bayesian Learning
• A random forest classifier from this chapter

We can combine these three algorithms into a voting classifier and apply it to the breast cancer dataset with the following steps:

1. Load the dataset, and split it into training and test sets:

In [1]: from sklearn.datasets import load_breast_cancer...     cancer = load_breast_cancer()...     X = cancer.data...     y = cancer.targetIn [2]: from sklearn.model_selection import train_test_split... X_train, ...