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

Maximum margin classification and support vectors


The following figure depicts instances from two linearly separable classes and three possible decision boundaries. All of the decision boundaries separate the training instances of the positive class from the training instances of the negative class, and a perceptron can learn any of them. Which of these decision boundaries is most likely to perform best on test data?

From this visualization, it is intuitive that the dotted decision boundary is the best. The solid decision boundary is near many of the positive instances. The test set could contain a positive instance that has a slightly smaller value for the first explanatory variable, x1; this instance would be classified incorrectly. The dashed decision boundary is farther away from most of the training instances; however, it is near one of the positive instances and one of the negative instances.

The previous figure provides a different perspective on evaluating decision boundaries. Assume...