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

Bagging


Bagging stands for bootstrap aggregation. Hence, it's clear that the baggingconceptstems from bootstrapping. It implies that bagging has got the elements of bootstrapping. It is a bootstrap ensemble method wherein multiple classifiers (typically from the same algorithm) are trained on the samples that are drawn randomly with replacements (bootstrap samples) from the training set/population. Aggregation of all the classifiers takes place in the form of average or by voting. It tries to reduce the affect of the overfitting issue in the model as shown in the following diagram:

There are three stages of bagging:

  • Bootstrapping: This is a statistical technique that's used to generate random samples or bootstrap samples with replacement.
  • Model fitting: In this stage, we build models on bootstrap samples. Typically, the same algorithm is used for building the models. However, there is no restriction on using different algorithms.
  • Combining models: This step involves combining all the models...