This tutorial will guide towards good evaluation of machine-learning models, choosing metrics and procedures that match the intended usage, with code examples using the latest scikit-learn's features. We will discuss how good metrics should characterize all aspects of error, e.g. on the positive and negative class; the probability of a detection, or the probability of a true event given a detection; as they may need to catter for class imbalance. Metrics may also evaluate confidence scores, e.g. calibration. Model-evaluation procedures should gauge not only the expected generalization performance, but also its variations.

This talk will cover how to build predictive models that handle well missing values, using scikit-learn. It will give on the one side the statistical considerations, both the classic statistical missing-values theory and the recent development in machine learning, and on the other side how to efficiently code solutions.