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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Created: July 25, 2014Englishfrançais