The present invention addresses two ubiquitous and pressing problems of modern data analytics technology. Many modern pattern recognition technologies produce models with excellent predictivity but (a) they are “black boxes”, that is they are opaque to the user; (b) they are too large, and/or expensive to execute in less powerful computing platforms. The invention “opens up” a black box model by converting it to a compact and understandable model that is functionally equivalent. The invention also converts a predictive model into a functionally equivalent model into a form that can be implemented and deployed more easily or efficiently in practice. The benefits include: model understandability and defensibility of modeling. A particularly interesting application is that of understanding the decision making of humans, comparison of the behavior of a human or computerized decision process against another and use to enhance education and guideline compliance/adherence detection and improvement. The invention can be applied to practically any field where predictive modeling (classification and regression) is desired because it relies on extremely broad distributional assumptions that are valid in numerous fields.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method and system for converting predictive models to equivalent models comprising the following steps: a. learning a model M1 from dataset D; b. generating a model B1 of the distribution of input variables in D; c. generating input patterns from B1 or D using statistical sampling; d. creating new data D1 that comprises of the generated inputs followed by the corresponding M1 model-estimated outputs; e. deriving all or multiple Markov Boundaries (MB1, . . . , MBn) of the response variable by application of appropriately instantiated TIE* method on D1; f. learning from each Markov Boundary (i.e., MBi), an equivalent representation DTi that is easier to understand by humans or is easier to implement in practical application settings; g. verifying and fine tuning each equivalent model to capture the outputs of M1 within acceptable accuracy e; h. keeping only the Markov Boundaries that satisfy the condition of step g; and i. outputting the catalogue of all validated models DTi comprising the complete final set of equivalent explanations of the function contained in model M1. 2. The computer-implemented method and system of claim 1 in which Bayesian network induction techniques are used in step b. 3. The computer-implemented method and system of claim 1 in which step c is implemented using one of the following methods: i. resampling from D with uniform probability with or without replacement; or ii. sampling joint patterns of input variables proportionately to the pattern probability; or iii. generating all high probability input patterns and sample over remaining ones proportionately to their probability; or iv. using logic sampling on B1 (i.e., uniformly randomly over space of joint input patterns); or any of a number of sampling methods that are commonly employed in statistics, engineering or pattern recognition to sample from a joint distribution. 4. The computer-implemented method and system of claim 1 in which step f uses as equivalent representation Decision Trees, rule sets, or their equivalent logical expressions and where these are learned either by direct application of decision trees learners, rule learners and logical expression learners or by learning decision trees and then converting them to rules and/or logical expressions. 5. The computer-implemented method and system of claim 1 in which: i. steps b, c, d being omitted; ii. a single Markov boundary being extracted in step e; iii. a single decision tree DT being created using standard decision tree induction on the original data D in step f; iv. the decision tree of step f being optionally pruned using standard pruning methods; v. verifying that the decision tree of step f closely captures the outputs of M1; vi. optionally converting the decision tree of step f into the equivalent rules or logical expressions; and outputting the decision tree and its equivalent rules or logical expressions. 6. The computer-implemented method and system of claim 1 in which the method is applied on data from several computer or human decision makers and then outputs are compared: i. establishing the input patterns for which decision makers agree or disagree; ii. establishing the inputs that lead to agreement and disagreement among decision makers; iii. establishing that a decision maker is consistent to, or compliant with a decision policy; and correcting decision makers by reference to input patterns that trigger false or non-compliant decisions. 7. The computer-implemented method and system of claim 5 in which the method is applied on data from several computer or human decision makers and then outputs are compared: i. establishing the input patterns for which decision makers agree or disagree; ii. establishing the inputs that lead to agreement and disagreement among decision makers; iii. establishing that a decision maker is consistent to, or compliant with a decision policy; and iv. correcting decision makers by reference to input patterns that trigger false or non-compliant decisions.