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)

Regression trees

You have learned how trees are used in order to classify a prediction as belonging to a particular class or category. However, trees can also be used to solve problems related to predicting numeric outcomes. In this section, you will learn about the three types of tree based algorithms that you can implement in scikit-learn in order to predict numeric outcomes, instead of classes:

  • The decision tree regressor
  • The random forest regressor
  • The gradient boosted tree

The decision tree regressor

When we have data that is non-linear in nature, a linear regression model might not be the best model to choose. In such situations, it makes sense to choose a model that can fully capture the non-linearity of such data...