Abstract: Techniques are provided for generating performance improvement recommendations for machine learning models. One method comprises evaluating performance metrics for multiple implementations of a machine learning model; computing a performance score that aggregates the performance metrics for a given machine learning model implementation; and recommending a modification to the given machine learning model implementation based on the performance score by evaluating one or more performance metrics for the given implementation relative to at least one additional performance metric for the given implementation, wherein the recommended modification is based on a performance with the recommended modification for another implementation. A given performance metric may be weighted based on an expected improvement from modifying a factor related to the given performance metric.

Abstract: Techniques are provided for generating adaptive policies from organization data for detection of risk-related events. One method comprises obtaining features identified in organization data of an organization for a risk analysis, wherein a given feature comprises a plurality of data values, wherein each data value for the given feature comprises a discrete value of the given feature or a range of values for the given feature; obtaining a probability of occurrence associated with each data value based on the organization data; identifying a plurality of candidate anomalous data values based on the probabilities of occurrence; determining an intervention rate for a plurality of combinations of the candidate anomalous data values; and generating policies for the organization using the combinations of candidate anomalous data values based on a corresponding intervention rate. The generated policies are used to detect one or more risk-related events.

Abstract: Techniques are provided for generating performance improvement recommendations for machine learning models. One method comprises evaluating performance metrics for multiple implementations of a machine learning model; computing a performance score that aggregates the performance metrics for a given machine learning model implementation; and recommending a modification to the given machine learning model implementation based on the performance score by evaluating one or more performance metrics for the given implementation relative to at least one additional performance metric for the given implementation, wherein the recommended modification is based on a performance with the recommended modification for another implementation. A given performance metric may be weighted based on an expected improvement from modifying a factor related to the given performance metric.

Abstract: Techniques are provided for generating adaptive policies from organization data for detection of risk-related events. One method comprises obtaining features identified in organization data of an organization for a risk analysis, wherein a given feature comprises a plurality of data values, wherein each data value for the given feature comprises a discrete value of the given feature or a range of values for the given feature; obtaining a probability of occurrence associated with each data value based on the organization data; identifying a plurality of candidate anomalous data values based on the probabilities of occurrence; determining an intervention rate for a plurality of combinations of the candidate anomalous data values; and generating policies for the organization using the combinations of candidate anomalous data values based on a corresponding intervention rate. The generated policies are used to detect one or more risk-related events.