Don't Rule Out Simple Models Prematurely: A Large Scale Benchmark Comparing Linear and Non-linear Classifiers in OpenML
A basic step for each data-mining or machine learning task is to determine which model to choose based on the problem and the data at hand. In this paper we investigate when non-linear classifiers outperform linear classifiers by means of a large scale experiment. We benchmark linear and non-linear versions of three types of classifiers (support vector machines; neural networks; and decision trees), and analyze the results to determine on what type of datasets the non-linear version performs better. To the best of our knowledge, this work is the first principled and large scale attempt to support the common assumption that non-linear classifiers excel only when large amounts of data are available.
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