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Data Science for Marketing Analytics - Second Edition

You're reading from  Data Science for Marketing Analytics - Second Edition

Product type Book
Published in Sep 2021
Publisher Packt
ISBN-13 9781800560475
Pages 636 pages
Edition 2nd Edition
Languages
Authors (3):
Mirza Rahim Baig Mirza Rahim Baig
Profile icon Mirza Rahim Baig
Gururajan Govindan Gururajan Govindan
Profile icon Gururajan Govindan
Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Profile icon Vishwesh Ravi Shrimali
View More author details

Table of Contents (11) Chapters

Preface
1. Data Preparation and Cleaning 2. Data Exploration and Visualization 3. Unsupervised Learning and Customer Segmentation 4. Evaluating and Choosing the Best Segmentation Approach 5. Predicting Customer Revenue Using Linear Regression 6. More Tools and Techniques for Evaluating Regression Models 7. Supervised Learning: Predicting Customer Churn 8. Fine-Tuning Classification Algorithms 9. Multiclass Classification Algorithms Appendix

Tree-Based Regression Models

In the preceding activity, you were able to identify the three most important features that could be used to predict customer spend. Now, imagine doing the same by removing each feature one at a time and finding out the RMSE. RFE aims to remove the redundant task of going over each feature by doing it internally, without forcing the user to put in the effort to do it manually.

So far, we have covered linear regression models. Now it's time to take it up a notch by discussing some tree-based regression models.

Linear models are not the only type of regression models. Another powerful technique is the use of regression trees. Regression trees are based on the idea of a decision tree. A decision tree is a bit like a flowchart, where, at each step, you ask whether a variable is greater than or less than some value. After flowing through several of these steps, you reach the end of the tree and receive an answer for what value the prediction should be...

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