Abstract: Customizing an automated machine learning system is provided. The system receives a request to establish computer-executable operations for use with machine learning on a data set. The system provides, for display via a graphical user interface on the client device, an indication of a set of computer-executable operations generated automatically for machine learning on the data set by the system responsive to the request. The system receives, from the client device via the graphical user interface, an indication to modify the set of computer-executable operations. The system establishes compatibility of the set of computer-executable operations responsive to the modification. The system constructs, responsive to establishment of the compatibility, the set of computer-executable operations for use with machine learning.

Abstract: Re-binning and smoothing an indicator table is provided. A system can identify a table generated a model trained with machine learning, the table including bins for ranges of values of a feature and coefficients that indicate a level of a target for the bins. The system can receive, via a graphical user interface from a client device, a request to modify bins of the table. The system can establish, responsive to the request, a spline to fit the table based at least in part on a cost function weighted based on a number of entries of the feature for the ranges of values of the feature. The system can generate, via the spline established based at least in part on the cost function, a second table including second bins and second coefficients. The system can generate data to cause the graphical user interface to include a graphic representation of the second table.

Abstract: This disclosure relates generally to artificial intelligence structured to generate models based on multimodal input. At least one aspect is directed to a system. The system can include a data processing system comprising memory and one or more processors to generate, by a first model trained using machine learning with input including one or more first features each associated with data structures having a plurality of distinct data types, one or more second features compatible with one of the distinct data types, generate, by a second model trained with input including the second features, a plurality of cluster classifications each compatible with one or more of the distinct data types, and cause a user interface to present one or more of the data structures rendered according to a spatial structure based on the second features and the cluster classifications.