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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Concepts
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Toc

Table of Contents (14) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 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...

Creating your own transformer


As the complexity and type of dataset changes, you might find that you can't find an existing feature extraction transformer that fits your needs. We will see an example of this in Chapter 7, Follow Recommendations Using Graph Mining, where we create new features from graphs.

A transformer is akin to a converting function. It takes data of one form as input and returns data of another form as output. Transformers can be trained using some training dataset, and these trained parameters can be used to convert testing data.

The transformer API is quite simple. It takes data of a specific format as input and returns data of another format (either the same as the input or different) as output. Not much else is required of the programmer.

The transformer API

Transformers have two key functions:

  • fit(): This takes a training set of data as input and sets internal parameters
  • transform(): This performs the transformation itself. This can take either the training dataset, or...
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