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)

Implementing the k-NN algorithm using scikit-learn

In the following section, we will implement the first version of the k-NN algorithm and assess its initial accuracy. When implementing machine learning algorithms using scikit-learn, it is always a good practice to implement algorithms without fine-tuning or optimizing any of the associated parameters first in order to evaluate how well it performs.

In the following section, you will learn how to do the following:

  • Split your data into training and test sets
  • Implement the first version of the algorithm on the data
  • Evaluate the accuracy of your model using a k-NN score

Splitting the data into training and test sets

The idea of training and test sets is fundamental to every...