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)

Naive Bayes

The Naive Bayes algorithm is a probabilistic learning method. It is known as Naive because it assumes that all events in this word are independent, which is actually quite rare. However, in spite of this assumption, the Naive Bayesian algorithm has proven over time to provide great performance in terms of its prediction accuracy.

The Bayesian probability theory is based on the principle that the estimated likelihood of an event or a potential outcome should be based on the evidence at hand across multiple trials. Bayes’ theorem provides a way to calculate the probability of a given class, given some knowledge about prior observations.

This can be written as follows:

The different elements of this theorem can be explained as follows:

  • p(class|observation): This is the probability that the class holds given the observation.
  • P(observation): This is the prior probability...