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R Data Mining

You're reading from  R Data Mining

Product type Book
Published in Nov 2017
Publisher Packt
ISBN-13 9781787124462
Pages 442 pages
Edition 1st Edition
Languages
Concepts

Table of Contents (22) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Why to Choose R for Your Data Mining and Where to Start A First Primer on Data Mining Analysing Your Bank Account Data The Data Mining Process - CRISP-DM Methodology Keeping the House Clean – The Data Mining Architecture How to Address a Data Mining Problem – Data Cleaning and Validation Looking into Your Data Eyes – Exploratory Data Analysis Our First Guess – a Linear Regression A Gentle Introduction to Model Performance Evaluation Don't Give up – Power up Your Regression Including Multiple Variables A Different Outlook to Problems with Classification Models The Final Clash – Random Forests and Ensemble Learning Looking for the Culprit – Text Data Mining with R Sharing Your Stories with Your Stakeholders through R Markdown Epilogue
Dealing with Dates, Relative Paths and Functions

Random forest


Hey there, did you hear the discussion between Mr. Clough and Mr Sheene? And who do you think was the next person Mr. Sheene talked to after Mr. Clough? Yeah, you are guessing right, it was me: Andy, I want the list on my desk in two hours. Mr. Sheene was actually quite upset by Mr. Clough suggesting that one of us spread the word about the analyses

That said, what we have to do now? Well, first of all, we still have to fit random forest on our data, in order to complete our data modelling strategy. Finally, we will employ all of our estimated valid models on the full list of customers pertaining to the Middle East area. 

The result of this application will be the list of customers enriched with our model prediction.

What? How are we going to merge predictions from our different models? We are going to leverage ensemble learning techniques for that. But let's keep things in their order—we still have to fit two more models, and time is running out. 

Time to hurry up now and fit...

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