Book Image

Ensemble Machine Learning Cookbook

By : Dipayan Sarkar, Vijayalakshmi Natarajan
Book Image

Ensemble Machine Learning Cookbook

By: Dipayan Sarkar, Vijayalakshmi Natarajan

Overview of this book

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.
Table of Contents (14 chapters)

Multiple linear regression

Multiple linear regression is a technique used to train a linear model, that assumes that there are linear relationships between multiple predictor variables () and a continuous target variable (). The general equation for a multiple linear regression with m predictor variables is as follows:

Training a linear regression model involves estimating the values of the coefficients for each of the predictor variables denoted by the letter . In the preceding equation, denotes an error term, which is normally distributed, and has zero mean and constant variance. This is represented as follows:

Various techniques can be used to build a linear regression model. The most frequently used is the ordinary least square (OLS) estimate. The OLS method is used to produce a linear regression line...