Adversarial validation, part one - FastML
Many data science competitions suffer from a test set being markedly different from a training set (a violation of the “identically distributed” assumption). It is then difficult to make a representative validation set. We propose a method for selecting training examples most similar to test examples and using them as a validation set. The core of this idea is training a probabilistic classifier t
Improve Your Model Performance using Cross Validation (in Python and R)
Improve Your Model Performance using Cross Validation (in Python and R) This article was originally published on November 18, 2015, and updated on April 30, 2018. Introduction One of the most interesting and challenging things about data science hackathons is getting a high score on both public and private leaderboards. I have closely monitored the series of data science hackathons and found an in
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