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  • Book Overview & Buying Mastering Machine Learning with R
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Mastering Machine Learning with R

Mastering Machine Learning with R - Second Edition

By : Cory Lesmeister, Doug Ortiz , Vikram Dhillon, Miroslav Kopecky
2.8 (4)
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Mastering Machine Learning with R

Mastering Machine Learning with R

2.8 (4)
By: Cory Lesmeister, Doug Ortiz , Vikram Dhillon, Miroslav Kopecky

Overview of this book

This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (17 chapters)
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Modeling

This is where all the work that you've done up to this point can lead to fist-pumping exuberance or fist-pounding exasperation. But hey, if it was that easy, everyone would be doing it. The tasks are as follows:

  1. Selecting a modeling technique.
  2. Generating a test design.
  3. Building a model.
  4. Assessing a model.

Oddly, this process step includes the considerations that you have already thought of and prepared for. In the first step, you will need at least some idea about how you will be modeling. Remember that this is a flexible, iterative process and not some strict linear flowchart such as an aircrew checklist.

The cheat sheet included in this chapter should help guide you in the right direction for the modeling techniques. Test design refers to the creation of your test and train datasets and/or the use of cross-validation and this should have been thought of and accounted for in the data preparation.

Model assessment involves comparing the models with the criteria/criterion that you developed in the business understanding, for example, RMSE, Lift, ROC, and so on.

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Mastering Machine Learning with R
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