Book Image

Machine Learning Quick Reference

By : Rahul Kumar
Book Image

Machine Learning Quick Reference

By: Rahul Kumar

Overview of this book

Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Chapter 3. Performance in Ensemble Learning

So far, we have learned that no two models will give the same result. In other words, different combinations of data or algorithms will result in a different outcome. This outcome can be good for a particular combination and not so good for another combination. What if we have a model that tries to take these combinations into account and comes up with a generalized and better result? This is called an ensemble model.

In this chapter, we will be learning about a number of concepts in regard to ensemble modeling, which are as follows:

  • Bagging
  • Random forest
  • Boosting
  • Gradient boosting
  • Optimization of parameters