Explanations of model predictions with live and breakDown packages

Predictive modelling is a very exciting field with many different applications. Lots of algorithms have been developed in this area. According to many Kaggle competitions (Fogg, 2016), winning solutions are often obtained with elastic tools like random forest, gradient boosting or neural networks. These algorithms have many strengths but also share a major weakness, which is the lack of interpretability of a model structure. A single random forest, an xgboost model or a neural network may be parametrized with thousands of parameters which makes these models hard to understand.