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Learning Data Mining with Python, - Second Edition

You're reading from Learning Data Mining with Python, - Second Edition

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Pages 358 pages

Edition 2nd Edition

Languages

Concepts

Table of Contents (20) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with Data Mining

2. Classifying with scikit-learn Estimators

3. Predicting Sports Winners with Decision Trees

4. Recommending Movies Using Affinity Analysis

5. Features and scikit-learn Transformers

6. Social Media Insight using Naive Bayes

7. Follow Recommendations Using Graph Mining

8. Beating CAPTCHAs with Neural Networks

9. Authorship Attribution

10. Clustering News Articles

11. Object Detection in Images using Deep Neural Networks

12. Working with Big Data

13. Next Steps...

scikit-learn estimators

Estimators that allows for the standardized implementation and testing of algorithms a common, lightweight interface for classifiers to follow. By using this interface, we can apply these tools to arbitrary classifiers, without needing to worry about how the algorithms work.

Estimators must have the following two important functions:

fit(): This function performs the training of the algorithm - setting the values of internal parameters. The fit() takes two inputs, the training sample dataset and the corresponding classes for those samples.
predict(): This the class of the testing samples that we provide as the only input. This function returns a NumPy array with the predictions of each input testing sample.

Most scikit-learn estimators use NumPy arrays or a related format for input and output. However this is by convention and not required to use the interface.

There are many estimators implemented in scikit-learn and more in other open source projects that use the same...

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