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Machine Learning with scikit-learn Quick Start Guide

You're reading from Machine Learning with scikit-learn Quick Start Guide

Product type Book

Published in Oct 2018

Publisher Packt

ISBN-13 9781789343700

Pages 172 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Kevin Jolly

Table of Contents (10) Chapters

Preface

1. Introducing Machine Learning with scikit-learn

2. Predicting Categories with K-Nearest Neighbors

3. Predicting Categories with Logistic Regression

4. Predicting Categories with Naive Bayes and SVMs

5. Predicting Numeric Outcomes with Linear Regression

6. Classification and Regression with Trees

7. Clustering Data with Unsupervised Machine Learning

8. Performance Evaluation Methods

9. Other Books You May Enjoy

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Regression trees

You have learned how trees are used in order to classify a prediction as belonging to a particular class or category. However, trees can also be used to solve problems related to predicting numeric outcomes. In this section, you will learn about the three types of tree based algorithms that you can implement in scikit-learn in order to predict numeric outcomes, instead of classes:

The decision tree regressor
The random forest regressor
The gradient boosted tree

The decision tree regressor

When we have data that is non-linear in nature, a linear regression model might not be the best model to choose. In such situations, it makes sense to choose a model that can fully capture the non-linearity of such data...

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Authors (1)

Kevin Jolly

Kevin Jolly is a formally educated data scientist with a master's degree in data science from the prestigious King's College London. Kevin works as a statistical analyst with a digital healthcare start-up, Connido Limited, in London, where he is primarily involved in leading the data science projects that the company undertakes. He has built machine learning pipelines for small and big data, with a focus on scaling such pipelines into production for the products that the company has built. Kevin is also the author of a book titled Hands-On Data Visualization with Bokeh, published by Packt. He is the editor-in-chief of Linear, a weekly online publication on data science software and products.

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