Book Image

Machine Learning with scikit-learn Quick Start Guide

By : Kevin Jolly
Book Image

Machine Learning with scikit-learn Quick Start Guide

By: Kevin Jolly

Overview of this book

Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Table of Contents (10 chapters)

Classification trees

Classification trees are used to predict a category or class. This is similar to the classification algorithms that you have learned about previously in this book, such as the k-nearest neighbors algorithm or logistic regression.

Broadly speaking, there are three tree based algorithms that are used to solve classification problems:

  • The decision tree classifier
  • The random forest classifier
  • The AdaBoost classifier

In this section, you will learn how each of these tree based algorithms works, in order to classify a row of data as a particular class or category.

The decision tree classifier

The decision tree is the simplest tree based algorithm, and serves as the foundation for the other two algorithms....