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scikit-learn Cookbook , Second Edition

You're reading from   scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Length 374 pages
Edition 2nd Edition
Languages
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Authors (2):
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Trent Hauck Trent Hauck
Author Profile Icon Trent Hauck
Trent Hauck
Julian Avila Julian Avila
Author Profile Icon Julian Avila
Julian Avila
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Toc

Table of Contents (13) Chapters Close

Preface 1. High-Performance Machine Learning – NumPy FREE CHAPTER 2. Pre-Model Workflow and Pre-Processing 3. Dimensionality Reduction 4. Linear Models with scikit-learn 5. Linear Models – Logistic Regression 6. Building Models with Distance Metrics 7. Cross-Validation and Post-Model Workflow 8. Support Vector Machines 9. Tree Algorithms and Ensembles 10. Text and Multiclass Classification with scikit-learn 11. Neural Networks 12. Create a Simple Estimator

Implementing random forest regression

Random forests is an ensemble algorithm. Ensemble algorithms use several algorithms together to improve predictions. Scikit-learn has several ensemble algorithms, most of which use trees to predict. Let's start by expanding on decision tree regression with several decision trees working together in a random forest.

A random forest is a mixture of several decision trees, where each tree provides a single vote toward the final prediction. The final random forest calculates a final output by averaging the results of all the trees it is composed of.

Getting ready

Load the diabetes regression dataset as we did with decision trees. Split all of the data into training and testing sets:

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