System and Method of Cyclic Boosting for Explainable Supervised Machine Learning

Abstract

A system and method are disclosed including a computer and a processor and memory. The computer receives historical sales data comprising aggregated sales data for one or more items from one or more store for at least one past time period. The computer further trains a cyclic boosting model to learn model parameters by iteratively calculating for each feature and each bin factors for at least one full feature cycle. The computer further predicts one or more demand quantities during a prediction period by applying a prediction model to historical supply chain data, wherein a training period is earlier than the prediction period, and each of the one or more demand quantities is associated with at least one item of the one or more items and at least one stocking location of the one or more stocking locations during the prediction period and rendering a demand prediction feature explanation visualization.

What is claimed is: 
     
         1 . A computer-implemented method for calculating model parameters of a cyclic boosting model, comprising:
 regularizing, by a computer comprising a processor and a memory, one or more factors in one or more categories of training data by determining Bayesian a priori probabilities for each occurrence of a specific category of a feature variable;   calculating, by the computer, a global average from all observed target values across all bins and features, wherein the bins and features are defined by a matrix;   initializing, by the computer, each factor of the one or more factors to 1, wherein each factor corresponds to a single bin and a single feature;   calculating iteratively, by the computer, for each feature and corresponding bin, partial factors and aggregate factors, wherein a partial factor is multiplied by an aggregate factor in each iteration of a multiple of iterations, and wherein calculation of the partial factor in each iteration includes a learning rate to reduce dependency on a sequence of features;   calculating, by the computer, a predicted value of a target variable for each of the multiple of iterations until a stopping criteria is met, wherein the calculated predicted values follow a Poisson or Poisson-Gamma distribution; and   rendering, for display on a user interface, a visualization comprising a two-dimensional visualization of one or more deviations between the calculated predicted values and corresponding actual values.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the stopping criteria comprises a mean absolute deviation or a mean squared error. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the one or more factors each correspond to a strength of a factor contributing to the predicted value. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the training data comprises one or more of:
 one or more historic sales patterns, one or more prices, one or more promotions, one or more weather conditions and one or more factors influencing demand of an item sold in a given store on a specific day.   
     
     
         5 . The computer-implemented method of  claim 1 , further comprising:
 receiving, by the computer, a selection from a user interface module of a list of one or more sample input variables, one or more sample input variable definitions and one or more sample input variable sequences.   
     
     
         6 . The computer-implemented method of  claim 1 , further comprising:
 receiving, by the computer, a modification to a feature variable to identify and calculate a corresponding change in a predicted value.   
     
     
         7 . The computer-implemented method of  claim 1 , further comprising:
 regularizing, by the computer, one or more factors in one or more categories of training data by assuming a Gamma distribution as a prior for a distribution of the one or more factors.   
     
     
         8 . A system for calculating model parameters of a cyclic boosting model, comprising:
 a server, comprising a processor and a memory, configured to:   regularize one or more factors in one or more categories of training data by determining Bayesian a priori probabilities for each occurrence of a specific category of a feature variable;   calculate a global average from all observed target values across all bins and features, wherein the bins and features are defined by a matrix;   initialize each factor of the one or more factors to  1 , wherein each factor corresponds to a single bin and a single feature;   calculate iteratively, for each feature and corresponding bin, partial factors and aggregate factors, wherein a partial factor is multiplied by an aggregate factor in each iteration of a multiple of iterations, and wherein calculation of the partial factor in each iteration includes a learning rate to reduce dependency on a sequence of features;   calculate a predicted value of a target variable for each of the multiple of iterations until a stopping criteria is met, wherein the calculated predicted values follow a Poisson or Poisson-Gamma distribution; and   render, for display on a user interface, a visualization comprising a two-dimensional visualization of one or more deviations between the calculated predicted values and corresponding actual values.   
     
     
         9 . The system of  claim 8 , wherein the stopping criteria comprises a mean absolute deviation or a mean squared error. 
     
     
         10 . The system of  claim 8 , wherein the one or more factors each correspond to a strength of a factor contributing to the predicted value. 
     
     
         11 . The system of  claim 8 , wherein the training data comprises one or more of:
 one or more historic sales patterns, one or more prices, one or more promotions, one or more weather conditions and one or more factors influencing demand of an item sold in a given store on a specific day.   
     
     
         12 . The system of  claim 8 , wherein the server is further configured to:
 receive a selection from a user interface module of a list of one or more sample input variables, one or more sample input variable definitions and one or more sample input variable sequences.   
     
     
         13 . The system of  claim 8 , wherein the server is further configured to:
 receive a modification to a feature variable to identify and calculate a corresponding change in a predicted value.   
     
     
         14 . The system of  claim 8 , wherein the server is further configured to:
 regularize one or more factors in one or more categories of training data by assuming a Gamma distribution as a prior for a distribution of the one or more factors.   
     
     
         15 . A non-transitory computer-readable medium embodied with software for calculating model parameters of a cyclic boosting model, wherein the software, when executed:
 regularizes one or more factors in one or more categories of training data by determining Bayesian a priori probabilities for each occurrence of a specific category of a feature variable;   calculates a global average from all observed target values across all bins and features, wherein the bins and features are defined by a matrix;   initializes each factor of the one or more factors to 1, wherein each factor corresponds to a single bin and a single feature;   calculates iteratively, for each feature and corresponding bin, partial factors and aggregate factors, wherein a partial factor is multiplied by an aggregate factor in each iteration of a multiple of iterations, and wherein calculation of the partial factor in each iteration includes a learning rate to reduce dependency on a sequence of features;   calculates a predicted value of a target variable for each of the multiple of iterations until a stopping criteria is met, wherein the calculated predicted values follow a Poisson or Poisson-Gamma distribution; and   renders, for display on a user interface, a visualization comprising a two-dimensional visualization of one or more deviations between the calculated predicted values and corresponding actual values.   
     
     
         16 . The non-transitory computer-readable medium of  claim 15 , wherein the stopping criteria comprises a mean absolute deviation or a mean squared error. 
     
     
         17 . The non-transitory computer-readable medium of  claim 15 , wherein the one or more factors each correspond to a strength of a factor contributing to the predicted value. 
     
     
         18 . The non-transitory computer-readable medium of  claim 15 , wherein the training data comprises one or more of:
 one or more historic sales patterns, one or more prices, one or more promotions, one or more weather conditions and one or more factors influencing demand of an item sold in a given store on a specific day.   
     
     
         19 . The non-transitory computer-readable medium of  claim 15 , wherein the software, when executed, further:
 receives a selection from a user interface module of a list of one or more sample input variables, one or more sample input variable definitions and one or more sample input variable sequences.   
     
     
         20 . The non-transitory computer-readable medium of  claim 15 , wherein the software, when executed, further:
 receives a modification to a feature variable to identify and calculate a corresponding change in a predicted value.