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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

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781788295758

Length 442 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Pratap Dangeti

View More author details

Table of Contents (16) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

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

Summary

In this chapter, you have learned the working principles of logistic regression and its step-by-step solving methodology by iteratively removing insignificant and multi-collinear variables to find the best fit by constantly checking AIC and concordance values to determine the best model in a statistical way. Subsequently we looked at machine learning model and random forest being applied to calculate the test accuracy. It was found that, by carefully tuning the hyperparameters of random forest using grid search, we were able to uplift the results by 10 percent in terms of test accuracy from 80 percent from logistic regression to 90 percent from random forest.

In the next chapter, we will be covering complete tree based models such as decision trees, random forest, boosted trees, ensemble of models, and so on to further improve accuracy!

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Authors (1)

Pratap Dangeti

Pratap Dangeti

Pratap Dangeti is currently working as a Senior Data Scientist at Bidgely Technologies Bangalore. He has a vast experience in analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.

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