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R Machine Learning By Example

You're reading from   R Machine Learning By Example Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Length 340 pages
Edition 1st Edition
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Author (1):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Machine Learning FREE CHAPTER 2. Let's Help Machines Learn 3. Predicting Customer Shopping Trends with Market Basket Analysis 4. Building a Product Recommendation System 5. Credit Risk Detection and Prediction – Descriptive Analytics 6. Credit Risk Detection and Prediction – Predictive Analytics 7. Social Media Analysis – Analyzing Twitter Data 8. Sentiment Analysis of Twitter Data Index

Feature selection


The process of feature selection involves ranking variables or features according to their importance by training a predictive model using them and then trying to find out which variables were the most relevant features for that model. While each model often has its own set of important features, for classification we will use a random forest model here to try and figure out which variables might be of importance in general for classification-based predictions.

We perform feature selection for several reasons, which include:

  • Removing redundant or irrelevant features without too much information loss

  • Preventing overfitting of models by using too many features

  • Reducing variance of the model which is contributed from excess features

  • Reducing training time and converging time of models

  • Building simple and easy to interpret models

We will be using a recursive feature elimination algorithm for feature selection and an evaluation algorithm using a predictive model where we repeatedly...

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