Abstract: There is a need for more effective and efficient predictive data analysis. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method for generating a neutralized prediction model includes accessing an initial prediction model generated using an initial training data object, performing a randomized shuffling of the initial training data object to generate a shuffled training data object, generating randomized predictions by processing the shuffled training data object using the initial prediction model, performing a neutralization of the initial training data object to generate a neutralized training data object, and generating the neutralized prediction model based at least in part on the neutralized training data object and the randomized predictions.

Abstract: An apparatus is provided for generating a generalized linear model structure definition by generating a gradient boosted tree model and separating each decision tree into a plurality of indicator variables upon which a dependent variable of the generalized linear model depends. A first number of plurality of decision tree structures each having a maximum tree depth of one (1) is formed, where the first number represents a number of decision tree structures necessary to exhaust all main effects of a plurality of predictor variables on a dependent variable. Successive pluralities of decision tree structures each having a maximum tree depth increased by one (1) as compared to its immediately preceding plurality of decision tree structures are iteratively formed. Each successive plurality of decision tree structures comprises a second number of decision tree structures necessary to exhaust all interactions between the plurality of predictor variables.

Abstract: An apparatus for generating a generalized linear model structure definition by generating a gradient boosted tree model and separating each decision tree into a plurality of indicator variables upon which a dependent variable of the generalized linear model depends.