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Feature Engineering Made Easy

You're reading from   Feature Engineering Made Easy Identify unique features from your dataset in order to build powerful machine learning systems

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781787287600
Length 316 pages
Edition 1st Edition
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Authors (2):
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Divya Susarla Divya Susarla
Author Profile Icon Divya Susarla
Divya Susarla
Sinan Ozdemir Sinan Ozdemir
Author Profile Icon Sinan Ozdemir
Sinan Ozdemir
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Table of Contents (10) Chapters Close

Preface 1. Introduction to Feature Engineering 2. Feature Understanding – What's in My Dataset? FREE CHAPTER 3. Feature Improvement - Cleaning Datasets 4. Feature Construction 5. Feature Selection 6. Feature Transformations 7. Feature Learning 8. Case Studies 9. Other Books You May Enjoy

Using RBMs in a machine learning pipeline


Of course, we want to see how the RBM performs in our machine learning pipelines to not just visualize the workings of the model, but to see concrete results of the feature learning. To do this, we will create and run three pipelines:

  • A logistic regression model by itself running on the raw pixel strengths
  • A logistic regression running on extracted PCA components
  • A logistic regression running on extracted RBM components

Each of these pipelines will be grid-searched across a number of components (for PCA and RBM) and the C parameter for logistic regression. Let's start with our simplest pipeline. We will run the raw pixel values through a logistic regression to see if the linear model is enough to separate out the digits.

Using a linear model on raw pixel values

To begin, we will run the raw pixel values through a logistic regression model in order to obtain something of a baseline model. We want to see if utilizing PCA or RBM components will allow the...

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