  Predictive analytics aims to build machine learning models to predict
behavior patterns and use predictions to guide decision-making. Predictive
analytics is human involved, thus the machine learning model is preferred to be
interpretable. In literature, Generalized Additive Model (GAM) is a standard
for interpretability. However, due to the one-to-many and many-to-one phenomena
which appear commonly in real-world scenarios, existing GAMs have limitations
to serve predictive analytics in terms of both accuracy and training
efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive
Model), a unified and fast interpretable model for predictive analytics. FXAM
extends GAM's modeling capability with a unified additive model for numerical,
categorical, and temporal features. FXAM conducts a novel training procedure
called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical,
categorical, and temporal features respectively. Each stage learns a local
optimum by fixing the parameters of other stages. We design joint learning over
categorical features and partial learning over temporal features to achieve
high accuracy and training efficiency. We prove that TSI is guaranteed to
converge to the global optimum. We further propose a set of optimization
techniques to speed up FXAM's training algorithm to meet the needs of
interactive analysis. Thorough evaluations conducted on diverse data sets
verify that FXAM significantly outperforms existing GAMs in terms of training
speed, and modeling categorical and temporal features. In terms of
interpretability, we compare FXAM with the typical post-hoc approach
XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM's
inherent interpretability for predictive analytics.
