Use Random Forest: Testing 179 Classifiers on 121 Datasets

Last Updated on July 31, 2020

If you don’t know what algorithm to use on your problem, try a few.

Alternatively, you could just try Random Forest and maybe a Gaussian SVM.

In a recent study these two algorithms were demonstrated to be the most effective when raced against nearly 200 other algorithms averaged over more than 100 data sets.

In this post we will review this study and consider some implications for testing algorithms on our own applied machine learning problems.

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Do We Need Hundreds of Classifiers

The title of the paper is “Do We Need Hundreds of Classifiers to Solve Real World Classification Problems?” and it was published in Journal of Machine Learning Research on October 2014.

In the paper, the authors evaluate 179 classifiers arising from 17 families
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