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)

Cluster visualization

Visualizing how your clusters are formed is no easy task when the number of variables/dimensions in your dataset is very large. There are two main methods that you can use in order to visualize how the clusters are distributed, as follows:

  • t-SNE: This creates a map of the dataset in two-dimensional space
  • Hierarchical clustering: This uses a tree-based visualization, known as a dendrogram, in order to create hierarchies

In this section, you will learn how to implement these visualization techniques, in order to create compelling cluster visuals.

t-SNE

The t-SNE is an abbreviation that stands for t-distributed stochastic neighbor embedding. The fundamental concept behind the t-SNE is to map a higher...