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)

Introduction to sampling

Sampling techniques can be broadly classified into non-probability sampling techniques and probability sampling techniques. Non-probability sampling techniques are based on the judgement of the user, whereas in probability sampling, the observations are selected by chance.

Probability sampling most often includes simple random sampling (SRS), stratified sampling, and systematic sampling:

  • SRS: In SRS, each observation in the population has an equal probability of being chosen for the sample.
  • Stratified sampling: In stratified sampling, the population data is divided into separate groups, called strata. A probability sample is then drawn from each group.
  • Systematic sampling: In this method, a sample is drawn from the population by choosing observations at regular intervals.
If the sample is too small or too large, it may lead to incorrect findings. For...