Machine Learning with scikit-learn Quick Start Guide | 8. Performance Evaluation Methods

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Machine Learning with scikit-learn Quick Start Guide is available from:

This is the “Code in Action” video for chapter 8 of Machine Learning with scikit-learn Quick Start Guide by Kevin Jolly, published by Packt. It includes the following topics:

00:17- Performance evaluation for classification algorithms
00:28- The confusion matrix
00:31- The normalized confusion matrix
00:38- Area under the curve
00:44- Cumulative gains curve
00:49- Lift curve
00:54- K-S statistic plot
01:00- Calibration plot
01:04- Learning curve
01:07- Cross-validated box plot
01:21- Performance evaluation for regression algorithms
01:25- Mean absolute error
01:27- Mean squared error
01:28- Root mean squared error
01:30- Performance evaluation for unsupervised algorithms
01:33- Elbow plot

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 the important machine learning algorithms that scikit-learn provides.
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Video created by Kevin Jolly
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