Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language by Daniel Whitenack

Author:Daniel Whitenack [Whitenack, Daniel]
Language: eng
Format: azw3, epub
Tags: COM004000 - COMPUTERS / Intelligence (AI) and Semantics, COM044000 - COMPUTERS / Neural Networks, COM042000 - COMPUTERS / Natural Language Processing
Publisher: Packt Publishing
Published: 2017-09-26T04:00:00+00:00

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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