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Julia for Data Science

You're reading from  Julia for Data Science

Product type Book
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Anshul Joshi Anshul Joshi
Profile icon Anshul Joshi
Toc

Table of Contents (17) Chapters close

Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. The Groundwork – Julia's Environment 2. Data Munging 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Random forests


Random forests were developed by Leo Breiman and Adele Cutler. Their strength in the field of machine learning has been shown nicely in a blog entry at Strata 2012: "Ensembles of decision trees (often known as random forests) have been the most successful general-purpose algorithm in modern times", as they "automatically identify the structure, interactions, and relationships in the data".

Moreover, it has been noticed that "most Kaggle solutions have no less than one top entry that vigorously utilizes this methodology". Random forests additionally have been the preferred algorithm for recognizing the body part in Microsoft's Kinect, which is a movement detecting information gadgets for Xbox consoles and Windows PCs.

Random forests comprises of a group of decision trees. We will consequently begin to analyze decision trees.

A decision tree, as discussed previously, is a tree-like chart where on each node there is a choice, in view of one single feature. Given an arrangement of...

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