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

Decision tree


A decision tree is a supervised learning technique that works on the divide-and-conquer approach. It can be used to address both classification and regression. The population undergoes a split into two or more homogeneous samples based on the most significant feature.

For example, let's say we have got a sample of people who applied for a loan from the bank. For this example, we will take the count as 50. Here, we have got three attributes, that is, gender, income, and the number of other loans held by the person, to predict whether to give them a loan or not.

We need to segment the people based on gender, income, and the number of other loans they hold and find out the most significant factor. This tends to create the most homogeneous set.

Let's take income first and try to create the segment based on it. The total number of people who applied for the loan is 50. Out of 50, the loan was awarded to 20 people. However, if we break this up by income, we can see that the breakup...