Abstract: This disclosure relates generally to using machine learning models to generate current time-series features using machine learning and validate time-series machine learning model output. At least one aspect is directed to a system with one or more processors, coupled to memory, to segment a time series range into a first segment for an instance of time, the segment associated with a value for a target feature and a timestamp for the value, segment the time series range into an input segment associated with a plurality of input features and a segment timestamp less than or equal to the timestamp, generate a model trained with input comprising values for the target feature and timestamps for the values less than or equal to the segment timestamp, and transform at least one of the input features based at least on the model.

Abstract: Systems and methods are described for interpreting machine learning model predictions. An example method includes: providing a machine learning model configured to receive a plurality of features as input and provide a prediction as output, wherein the plurality of features includes an engineered feature including a combination of two or more parent features; calculating a Shapley value for each feature in the plurality of features; and allocating a respective portion of the Shapley value for the engineered feature to each of the two or more parent features.

Abstract: Systems and methods are described for interpreting machine learning model predictions. An example method includes: providing a machine learning model configured to receive a plurality of features as input and provide a prediction as output, wherein the plurality of features includes an engineered feature including a combination of two or more parent features; calculating a Shapley value for each feature in the plurality of features; and allocating a respective portion of the Shapley value for the engineered feature to each of the two or more parent features.