Machine Learning, Xgboost, Financial Data, Model Selection.
the training data and then use the feature_importances_ attribute to discover the features that retain the most explanatory variance, while minimizing the number of features. At a certain point, some features yield less explanatory power than they are worth in terms of complexity. The plot below