In this chapter, we introduced ensembles. An ensemble is a combination of models that performs better than each of its components. We discussed three methods of training ensembles. Bootstrap aggregating, or bagging, can reduce the variance of an estimator; bagging uses bootstrap resampling to create multiple variants of the training set. The predictions of models trained on these variants are then averaged. Bagged decision trees are called random forests. Boosting is an ensemble meta-estimator that reduces the bias of its base estimators. AdaBoost is a popular boosting algorithm that iteratively trains estimators on training data that is weighted according to the previous estimators' errors. Finally, in stacking a meta-estimator learns to combine the predictions of heterogeneous base estimators.
Mastering Machine Learning with scikit-learn - Second Edition
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Mastering Machine Learning with scikit-learn - Second Edition
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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.
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Current Title:
Mastering Machine Learning with scikit-learn - Second Edition
Table of Contents (22 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
The Fundamentals of Machine Learning
Simple Linear Regression
Classification and Regression with k-Nearest Neighbors
Feature Extraction
From Simple Linear Regression to Multiple Linear Regression
From Linear Regression to Logistic Regression
Naive Bayes
Nonlinear Classification and Regression with Decision Trees
From Decision Trees to Random Forests and Other Ensemble Methods
The Perceptron
From the Perceptron to Support Vector Machines
From the Perceptron to Artificial Neural Networks
K-means
Dimensionality Reduction with Principal Component Analysis
Index
Customer Reviews