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1.发明申请公开(公告)号：US20190213685A1
公开(公告)日：2019-07-11
申请号：US16147154
申请日：2018-09-28
发明人： Brian Ironside
CPC分类号： G06Q40/08 , G06K9/6256 , G06K9/6282 , G06N20/00
摘要： 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.
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公开(公告)号：US10977737B2
公开(公告)日：2021-04-13
申请号：US16147154
申请日：2018-09-28
发明人： Brian Ironside
摘要： 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.
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公开(公告)号：US11954603B1
公开(公告)日：2024-04-09
申请号：US16850630
申请日：2020-04-16
发明人： Patrick Ford , Brian Ironside
摘要： 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.
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