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An Introduction to Decision Trees with Julia

POSTED IN: Data Analytics & Visualization Blog

See the Irises through the Decision Trees - using Julia  

Nice little article - good if you're familiar with IRIS data set, but interested in Julia; or just want to learn a little more about classification..

Data Science, Open Source -  Ben Sadeghi "Decision trees have played a significant role in data mining and machine learning since the 1960′s. They generate white-box classification and regression models which can be used for feature selection and sample prediction. The transparency of these models is a big advantage over black-box learners, in that the models are easy to understand and interpret, and that they can be readily extracted and implemented into any programming language (with nested if-else statements) for use in production environments. Furthermore, decision trees require almost no data preparation (i.e. normalization) and can handle both numerical and nominal/categorical data. Decision trees can also be pruned or bundled into ensembles of trees (i.e. random forests) in order to remedy over-fitting and improve prediction accuracy....

In this post, we’ll get some hands-on experience with decision trees and random forests applied to Fisher’s classic Iris dataset using a package written in the Julia language."

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About the Author

Ryan Anderson

Ryan Anderson

Hi! I like to play with data, analytics and hack around with robots and gadgets in my garage. Lately I've been learning about machine learning.

About this blog

Description is...<br/>Data Analytics & Visualization Blog - Generating insights from Data since 2013

Created: July 25, 2014


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