Abstract: Presented herein are methods and systems for generating and executing applications that provide insights to a model's operation without requiring the user to have knowledge of coding, computer programming, or artificial intelligence machine-learning methodologies. An exemplary method includes deploying a model using input data to generate a predicted dataset; presenting indications for a plurality of applications associated with the deployed model including an configured to generate new scenarios and another application configured to optimize at least one feature; presenting a plurality of features analyzed by the model; and in response to receiving a selection of a feature of the plurality of features and a new value for the feature, executing the first application to generate a second predicted dataset using the new value.

Abstract: A system to generate scenarios by modifying values of machine learning features is provided. The system can present a first indication in a first coordinate space of a first performance generated by a model trained with a plurality of features using machine learning. The system can present a second indication in a second coordinate space of a first performance of the first feature. The system can receive a modification to a value in the second coordinate space of the first feature. The system can determine a second performance of the model using machine learning based on a first derived feature to output derived data points in the time period. The system can present in the first coordinate space, a third indication of the second performance of the model overlaid with the first indication of the first performance of the model.

Abstract: Aspects of this technical solution can identify, by a second machine learning model receiving as input first features, second features having respective impact metrics that satisfy an impact threshold, the impact threshold indicating that the second features modify various forecast data points, cause a graphical user interface to present the forecast including one or more of the first features having respective first visual properties corresponding to identifiers of respective ones of the first features, cause the graphical user interface to present the forecast including the second features having a second visual property corresponding to an indication that the second features satisfy the impact threshold, and cause the graphical user interface to modify the forecast including the second features to include an explanation portion including metrics of the second features, the metrics corresponding to respective time points of a time dependency relationship.