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Applied Supervised Learning with Python

You're reading from Applied Supervised Learning with Python Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Product type Paperback

Published in Apr 2019

Publisher

ISBN-13 9781789954920

Length 404 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Ishita Mathur

Benjamin Johnston

View More author details

Table of Contents (9) Chapters

Applied Supervised Learning with Python

Preface

1. Python Machine Learning Toolkit FREE CHAPTER

2. Exploratory Data Analysis and Visualization

3. Regression Analysis

4. Classification

5. Ensemble Modeling

6. Model Evaluation

Appendix

Chapter 5. Ensemble Modeling

Note

Learning Objectives

By the end of the chapter, you will be able to:

Explain the concepts of bias and variance and how they lead to underfitting and overfitting
Explain the concepts behind bootstrapping
Implement a bagging classifier using decision trees
Implement adaptive boosting and gradient boosting models
Implement a stacked ensemble using a number of classifiers

Note

This chapter covers bias and variance, and underfitting and overfitting, and then introduces ensemble modeling.

The rest of the chapter is locked

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Authors (2)

Benjamin Johnston

Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.

See other products by Benjamin Johnston

Mathur

Mathur

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.

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