Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Evaluating the accuracy using cross-validation

The cross-validation is an important concept in machine learning. In the previous recipe, we split the data into training and testing datasets. However, in order to make it more robust, we need to repeat this process with different subsets. If we just fine-tune it for a particular subset, we may end up overfitting the model. Overfitting refers to a situation where we fine-tune a model too much to a dataset and it fails to perform well on unknown data. We want our machine learning model to perform well on unknown data.

Getting ready…

Before we discuss how to perform cross-validation, let's talk about performance metrics. When we are dealing with machine learning models, we usually care about three things: precision, recall, and F1 score. We can get the required performance metric using the parameter scoring. Precision refers to the number of items that are correctly classified as a percentage of the overall number of items in the list. Recall...