machine learning

Cross Validation done wrong

Cross validation is an essential tool in statistical learning to estimate the accuracy of your algorithm. Despite its great power it also exposes some fundamental risk when done wrong which may terribly bias your accuracy estimate. In this blog post I’ll demonstrate – using the Python scikit-learn framework – how to avoid the biggest and Cross Validation done wrong