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

[tsubuo]

“Adversarial validation”

[yubessy]

訓練データとテストデータをなるべく乖離しないように選ぶために、訓練データかテストデータかを分類する分類器を作る。面白い