Hybrid Machine Learning

A system comprising:

• one or more processors;
• and
• a computer-readable storage medium storing instructions that are executable by the one or more processors to:
• generate, automatically and independent of user input, a trained predictive computational model using a dataset and an automatic modeling application, the trained predictive computational model configured to generate an output based on new data;
• display, in a user interface of a computing device, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model using the dataset;
• display, in the user interface, at least one prompt for feedback corresponding to at least one decision in the sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model;
• receive, via the user interface, user input that modifies the at least one decision in response to the at least one prompt for feedback;
• and
• generate, using the automatic modeling application, a refined trained predictive computational model using a modified sequence of decisions that is defined by the user input provided in response to the at least one prompt for feedback.

A non-transitory computer-readable storage medium storing instructions that are executable by at least one processing device to perform operations comprising:

• generating, automatically and independent of user input, a trained predictive computational model using a dataset and an automatic modeling application, the trained predictive computational model configured to generate an output based on new data;
• displaying, in a user interface of a computing device, a representation of a pipeline that depicts a sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model using the dataset;
• displaying, in the user interface, at least one prompt for feedback corresponding to at least one decision in the sequence of decisions made by the automatic modeling application as part of generating the trained predictive computational model;
• receiving, via the user interface, user input that modifies the at least one decision in response to the at least one prompt for feedback;
• and
• causing the automatic modeling application to generate a refined trained predictive computational model using a modified sequence of decisions that is defined by the user input provided in response to the at least one prompt for feedback.