apyxl documentation

The apyxl package (Another PYthon package for eXplainable Learning) is a simple wrapper around xgboost, hyperopt, and shap. It provides the user with the ability to build a performant regression or classification model and use the power of the SHAP analysis to gain a better understanding of the links the model builds between its inputs and outputs. With apyxl, processing categorical features, fitting the model using Bayesian hyperparameter search, and instantiating the associated SHAP explainer can all be accomplished in a single line of code, streamlining the entire process from data preparation to model explanation.

The core of this package lies in the classes XGBClassifierWrapper and XGBRegressorWrapper. However, apyxl is not limited to these, as they also feed into the TimeSeriesNormalizer class, which enables the calculation of complex time series trends in an unsupervised manner.

More broadly, apyxl shapes my thinking on the connections between explainable machine learning, econometrics (Difference-In-Differences, Regression Discontinuity Design, Panel Analysis), time series normalization, and A/B testing.