Method and system of mitigating predictive multiplicity in a gradient boosting model

A computing apparatus for mitigating predictive multiplicity in a gradient boosting model (GBM), comprising:

• a processor;
• a memory;
• a display;
• and
• a communication interface coupled to each of the processor, the memory, and the display, wherein the processor is configured to:
• generate an empirical parameter set based on an approximation search related to a full parameter set resulting in a subset comprising candidates of at least one weak learner model (WLM) from a predetermined set of WLMs;
• train iteratively the empirical parameter set to derive a group filtered from the subset based on at least one from among a model selection (MS) technique via reweighted loss and an aggregation technique via an intermediate ensembles (IE) technique, wherein the group comprises the at least one WLM operating with at least one from among a minimal error from the subset and an additive weighted sum;
• select sequentially the at least one WLM from the derived group based on the at least one from among the MS technique and the IE technique;
• and
• generate the GBM based on a compilation of the sequentially selected at least one WLM, wherein the generated GBM operates below a minimum predefined disagreement threshold related to assessing predictive multiplicity, thereby mitigating the predictive multiplicity in the GBM.

A non-transitory computer readable storage medium storing instructions for mitigating predictive multiplicity in a gradient boosting model (GBM), the non-transitory computer readable storage medium comprising executable code which, when executed by a processor, causes the processor to:

• generate an empirical parameter set based on an approximation search related to a full parameter set resulting in a subset comprising candidates of at least one weak learner model (WLM) from a predetermined set of WLMs;
• train iteratively the empirical parameter set to derive a group filtered from the subset based on at least one from among a model selection (MS) technique via reweighted loss and an aggregation technique via an intermediate ensembles (IE) technique, wherein the group comprises the at least one WLM operating with at least one from among a minimal error from the subset and an additive weighted sum;
• select sequentially the at least one WLM from the derived group based on the at least one from among the MS technique and the IE technique;
• and
• generate the GBM based on a compilation of the sequentially selected at least one WLM, wherein the generated GBM operates below a minimum predefined disagreement threshold related to assessing predictive multiplicity, thereby mitigating the predictive multiplicity in the GBM.