摘要： How do Bitcoin markets behave? What are the causes of the sudden spikes and dips in cryptocurrency values? Are the markets for different altcoins inseparably linked or largely independent? How can we predict what will happen next?
摘要： In this second article on adversarial validation we get to the meat of the matter: what we can do when train and test sets differ. Will we be able to make a better validation set?
摘要： 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 distinguish train/test examples.
