Adversarial validation, part one - FastML

Adversarial validation, part one - FastML

Many data science competitions suffer from a test set being markedly different from a training se...概要を表示 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 to

• 機械学習
• Machine Learning
• あとで読む