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Machine Learning With Go

You're reading from   Machine Learning With Go Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Length 304 pages
Edition 1st Edition
Languages
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Author (1):
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Joseph Langstaff Whitenack Joseph Langstaff Whitenack
Author Profile Icon Joseph Langstaff Whitenack
Joseph Langstaff Whitenack
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Table of Contents (11) Chapters Close

Preface 1. Gathering and Organizing Data FREE CHAPTER 2. Matrices, Probability, and Statistics 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

Decision trees and random forests

Tree-based models are very different from the previous types of models that we have discussed, but they are widely utilized and very powerful. You can think about a decision tree model like a series of if-then statements applied to your data. When you train this type of model, you are constructing a series of control flow statements that eventually allow you to classify records.

Decision trees are implemented in github.com/sjwhitworth/golearn and github.com/xlvector/hector, among others, and random forests are implemented in github.com/sjwhitworth/golearn, github.com/xlvector/hector, and github.com/ryanbressler/CloudForest, among others. We will utilize github.com/sjwhitworth/golearn again in our examples shown in the following section.

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