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Statistics for Machine Learning

You're reading from   Statistics for Machine Learning Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Length 442 pages
Edition 1st Edition
Languages
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Author (1):
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Pratap Dangeti Pratap Dangeti
Author Profile Icon Pratap Dangeti
Pratap Dangeti
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Table of Contents (10) Chapters Close

Preface 1. Journey from Statistics to Machine Learning FREE CHAPTER 2. Parallelism of Statistics and Machine Learning 3. Logistic Regression Versus Random Forest 4. Tree-Based Machine Learning Models 5. K-Nearest Neighbors and Naive Bayes 6. Support Vector Machines and Neural Networks 7. Recommendation Engines 8. Unsupervised Learning 9. Reinforcement Learning

Parallelism of Statistics and Machine Learning

At first glance, machine learning seems to be distant from statistics. However, if we take a deeper look into them, we can draw parallels between both. In this chapter, we will deep dive into the details. Comparisons have been made between linear regression and lasso/ridge regression in order to provide a simple comparison between statistical modeling and machine learning. These are basic models in both worlds and are good to start with.

In this chapter, we will cover the following:

  • Understanding of statistical parameters and diagnostics
  • Compensating factors in machine learning models to equate statistical diagnostics
  • Ridge and lasso regression
  • Comparison of adjusted R-square with accuracy
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