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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 FREE CHAPTER 2. Predicting Categories with K-Nearest Neighbors 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

The k-NN algorithm

Mathematically speaking, the k-NN algorithm is one of the most simple machine learning algorithms out there. See the following diagram for a visual overview of how it works:

How k-NN works under the hood

The stars in the preceding diagram represent new data points. If we built a k-NN algorithm with three neighbors, then the stars would search for the three data points that are closest to it.

In the lower-left case, the star sees two triangles and one circle. Therefore, the algorithm would classify the star as a triangle since the number of triangles was greater than the number of circles.

In the upper-right case, the star sees two circles and one circle. Therefore, the algorithm will classify the star as a circle since the number of circles was greater than the number of triangles.

The real algorithm does this in a very probabilistic manner and picks the...

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