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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

Modeling using decision trees


Decision trees are algorithms which again belong to the supervised machine learning algorithms family. They are also used for both classification and regression, often called CART, which stands for classification and regression trees. These are used a lot in decision support systems, business intelligence, and operations research.

Decision trees are mainly used for making decisions that would be most useful in reaching some objective and designing a strategy based on these decisions. At the core, a decision tree is just a flowchart with several nodes and conditional edges. Each non-leaf node represents a conditional test on one of the features and each edge represents an outcome of the test. Each leaf node represents a class label where predictions are made for the final outcome. Paths from the root to all the leaf nodes give us all the classification rules. Decision trees are easy to represent, construct, and understand. However, the drawback is that they are...

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