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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

Performance Evaluation Methods

Your method of performance evaluation will vary by the type of machine learning algorithm that you choose to implement. In general, there are different metrics that can potentially determine how well your model is performing at its given task for classification, regression, and unsupervised machine learning algorithms.

In this chapter, we will explore how the different performance evaluation methods can help you to better understand your model. The chapter will be split into three sections, as follows:

  • Performance evaluation for classification algorithms
  • Performance evaluation for regression algorithms
  • Performance evaluation for unsupervised algorithms
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