Abstract: Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enable improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation.

Abstract: Systems and methods are disclosed for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enables improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. Computerized optimization techniques applied via variation generation across a dataset of test input records enable optimization for feature variation along with outcome variation. Moreover, the disclosed techniques may provide and/or realize a minimized variation to input data that correspond to a point of transition from one state to another state in an outcome that results from the input data, where the transition to another state is termed a “significant” variation to the output data.

Abstract: Disclosed are systems and methods for scenario planning by using specially programmed software engines to simulate and detect particular feature variations leading to particular outcomes based on modeling with machine learning techniques. The disclosed technology enables improved model debugging, improved simulation efficiency and accuracy, improved model explainability, improved identification of high risk or high reward scenarios, among other improvements and combinations thereof. In some embodiments, the disclosed technology implements computerized optimization techniques applied via variation generation across a dataset of test input records to optimize for feature variation along with outcome variation.

Abstract: Systems and methods for smart instance selection in accordance with embodiments of the invention are illustrated. One embodiment includes a system for selecting explanatory instances in datasets, including a processor, and a memory, the memory containing an instance selection application that configures the processor to: obtain a dataset comprising a plurality of records, obtain a machine learning model configured to classify records, initialize an explainer model, select at least one key instance from the dataset estimated to have explanatory power when provided to the explainer model, provide the explainer model with the selected at least one key instance; and provide an explanation produced by the explainer model.