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Statistics for Machine Learning

You're reading from  Statistics for Machine Learning

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781788295758
Pages 442 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Pratap Dangeti Pratap Dangeti
Profile icon Pratap Dangeti
LinkedIn
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
See other products by Pratap Dangeti

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 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

Comparison between logistic regression and decision trees

Before we dive into the coding details of decision trees, here, we will quickly compare the differences between logistic regression and decision trees, so that we will know which model is better and in what way.

Logistic regression

Decision trees

Logistic regression model looks like an equation between independent variables with respect to its dependent variable.

Tree classifiers produce rules in simple English sentences, which can be easily explained to senior management.

Logistic regression is a parametric model, in which the model is defined by having parameters multiplied by independent variables to predict the dependent variable.

Decision Trees are a non-parametric model, in which no pre-assumed parameter exists. Implicitly performs variable screening or feature selection.

Assumptions are made on response (or dependent) variable, with binomial or Bernoulli distribution.

No assumptions are made on the underlying distribution of the data...

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Authors (1)

Profile icon Pratap Dangeti
LinkedIn
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
See other products by Pratap Dangeti

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