Book Image

Mastering Machine Learning Algorithms

Book Image

Mastering Machine Learning Algorithms

Overview of this book

Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn v0.19.1. You will also learn how to use Keras and TensorFlow 1.x to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need.
Table of Contents (22 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
13
Deep Belief Networks
Index

Chapter 8. Ensemble Learning

In this chapter, we are going to discuss some important algorithms that exploit different estimators to improve the overall performance of an ensemble or committee. These techniques work either by introducing a medium level of randomness in every estimator belonging to a predefined set or by creating a sequence of estimators where, each new model is forced to improve the performance of the previous ones. These techniques allow us to reduce both the bias and the variance (thereby increasing validation accuracy) when employing models with a limited capacity or more prone to overfit the training set.

In particular, the topics covered in the chapter are as follows:

  • Introduction to ensemble learning
  • A brief introduction to decision trees
  • Random forest and extra randomized forests
  • AdaBoost (algorithms M1, SAMME, SAMME.R, and R2)
  • Gradient boosting
  • Ensembles of voting classifiers, stacking, and bucketing