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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

You're reading from   Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Length 384 pages
Edition 1st Edition
Languages
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Author (1):
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Tarek Amr Tarek Amr
Author Profile Icon Tarek Amr
Tarek Amr
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Supervised Learning
2. Introduction to Machine Learning FREE CHAPTER 3. Making Decisions with Trees 4. Making Decisions with Linear Equations 5. Preparing Your Data 6. Image Processing with Nearest Neighbors 7. Classifying Text Using Naive Bayes 8. Section 2: Advanced Supervised Learning
9. Neural Networks – Here Comes Deep Learning 10. Ensembles – When One Model Is Not Enough 11. The Y is as Important as the X 12. Imbalanced Learning – Not Even 1% Win the Lottery 13. Section 3: Unsupervised Learning and More
14. Clustering – Making Sense of Unlabeled Data 15. Anomaly Detection – Finding Outliers in Data 16. Recommender System – Getting to Know Their Taste 17. Other Books You May Enjoy

Detecting outliers using EllipticEnvelope

"I'm intimidated by the fear of being average."
– Taylor Swift

The EllipticEnvelope algorithm finds the center of the data samples and then draws an ellipsoid around that center. The radii of the ellipsoid in each axis are measured in the Mahalanobis distance. You can think of the Mahalanobis distance as a Euclideandistance whose units are the number of standard deviations in each direction. After the ellipsoid is drawn, the points that fall outside it can be considered outliers.

The multivariate Gaussian distribution is a key concept of the EllipticEnvelope algorithm. It's a generalization of the one-dimensional Gaussian distribution. If the Gaussian distribution is defined by single-valued mean and variance, then the multivariate Gaussian distribution is defined by matrices for means and covariances. The multivariate Gaussian distribution is then used to draw an ellipsoid that defines...
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