Abstract: A computing device selects a feature set and hyperparameters for a machine learning model to predict a value for a characteristic in a scoring dataset. A number of training model iterations is determined. A unique evaluation pair is selected for each iteration that indicates a feature set selected from feature sets and a hyperparameter configuration selected from hyperparameter configurations. A machine learning model is trained using each unique evaluation pair. Each trained machine learning model is validated to compute a performance measure value. An estimation model is trained with the feature set, the hyperparameter configuration, and the performance measure value computed for unique evaluation pair. The trained estimation model is executed to compute the performance measure value for each unique evaluation pair. A final feature set and a final hyperparameter configuration are selected based on the computed performance measure value.

Abstract: A computing device selects a feature set and hyperparameters for a machine learning model to predict a value for a characteristic in a scoring dataset. A number of training model iterations is determined. A unique evaluation pair is selected for each iteration that indicates a feature set selected from feature sets and a hyperparameter configuration selected from hyperparameter configurations. A machine learning model is trained using each unique evaluation pair. Each trained machine learning model is validated to compute a performance measure value. An estimation model is trained with the feature set, the hyperparameter configuration, and the performance measure value computed for unique evaluation pair. The trained estimation model is executed to compute the performance measure value for each unique evaluation pair. A final feature set and a final hyperparameter configuration are selected based on the computed performance measure value.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: Computer-implemented systems and methods are provided for generating bins for a scorecard. An approximate set of bins is generated by applying an optimization model to binning data. The optimization model includes an objective function, constraints, and surrogate weight of evidence metric(s). The approximated set of bins are then used in scorecard operations.

Abstract: Computer-implemented systems and methods are provided for generating bins for a scorecard. An approximate set of bins is generated by applying an optimization model to binning data. The optimization model includes an objective function, constraints, and surrogate weight of evidence metric(s). The approximated set of bins are then used in scorecard operations.