Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By : Gavin Hackeling
Book Image

Mastering Machine Learning with scikit-learn - Second Edition

By: Gavin Hackeling

Overview of this book

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model’s performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
9
From Decision Trees to Random Forests and Other Ensemble Methods
Index

Training decision trees


Let's create a decision tree using an algorithm called Iterative Dichotomiser 3(ID3). Invented by Ross Quinlan, ID3 was one of the first algorithms used to train decision trees. Assume that you are tasked with classifying animals as cats or dogs. Unfortunately, you cannot observe the animals directly and must use only a few attributes of the animals to make your decision. For each animal, you are told whether or not it likes to play fetch, whether or not it is frequently grumpy, and its favorite of three types of food.To classify new animals, the decision tree will examine a featureat each node. The edge it follows to the next node will depend on the outcome of the test. For example, the first node might ask whether or not the animal likes to play fetch. If the animal does, we will follow the edge to the left child node; if not, we will follow the edge to the right child node. Eventually an edge will connect to a leaf node that indicates whether the animal is a cat...