Mastering Machine Learning with scikit-learn文档.pdf

Mastering Machine Learning with scikit-learn Gavin Hackeling Chapter No. 5 Nonlinear Classification and Regression with Decision Trees In this package, you will find: The author’s biography A preview chapter from the book, Chapter no.5 Nonlinear Classification and Regression with Decision Trees A synopsis of the book’s content Information on where to buy this book About the Author Gavin Hackeling develops machine learning services for large-scale documents and image classification at an advertising network in New York. He received his Masters degree from New York Universitys Interactive Telecommunications Program, and his Bachelors degree from the University of North Carolina. To Hallie, for her support, and Zipper, without whose contributions this book would have been completed in half the time. For More Information: /big-data-and-business-intelligence/mastering-machine- learning-scikit-learn Mastering Machine Learning with scikit-learn Recent years have seen the rise of machine learning, the study of software that learns from experience. While machine learning is a new discipline, it has found many applications. We rely on some of these applications daily; in some cases, their successes have already rendered them mundane. Many other applications have only recently been conceived, and hint at machine learnings potential. In this book, we will examine several machine learning models and learning algorithms. We will discuss tasks that machine learning is commonly applied to, and learn to measure the performance of machine learning systems. We will work with a popular library for the Python programming language called scikit-learn, which has assembled excellent implementations of many machine learning models and algorithms under a simple yet versatile API. This book is motivated by two goal