A Unified and Fast Interpretable Model for Predictive Analytics
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In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM's (Generalized Additive Model) modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). The three stages correspond to learning over numerical, categorical and temporal features respectively. Each stage learns a local optimum by fixing 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 global optimum. We further propose a set of optimization techniques to speed up FXAM's training algorithm to meet the needs of interactive analysis. Evaluations verify that FXAM significantly outperforms existing GAMs in terms of training speed and modeling categorical and temporal features.
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