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

Bagging


Bootstrap aggregating, or bagging, is an ensemble meta-algorithm that can reduce the variance in an estimator. Bagging can be used in classification and regression tasks. When the component estimators are regressors, the ensemble averages their predictions. When the component estimators are classifiers, the ensemble returns the mode class.

Bagging independently fits multiple models on variants of the training data. The training data variants are created using a procedure called bootstrap resampling. Often it is necessary to estimate a parameter of an unknown probability distribution using only a sample of the distribution. We can use this sample to calculate a statistic, but we know that this statistic will vary according to the sample we happened to draw. Bootstrap resampling is a method of estimating the uncertainty in a statistic. It can only be used if the observations in the sample are drawn independently. Bootstrap resampling produces multiple variants of the sample by repeatedly...