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Machine Learning with scikit-learn Quick Start Guide

You're reading from   Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Length 172 pages
Edition 1st Edition
Languages
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Introducing Machine Learning with scikit-learn 2. Predicting Categories with K-Nearest Neighbors FREE CHAPTER 3. Predicting Categories with Logistic Regression 4. Predicting Categories with Naive Bayes and SVMs 5. Predicting Numeric Outcomes with Linear Regression 6. Classification and Regression with Trees 7. Clustering Data with Unsupervised Machine Learning 8. Performance Evaluation Methods 9. Other Books You May Enjoy

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...

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