Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for enhanced predictive modeling. One of the methods includes obtaining a first prediction from a machine learning model trained to predict future events based on time-series data input; generating a set of one or more features of the first prediction; determining that the set of one or more features of the first prediction satisfy one or more thresholds indicating an event included in the first prediction is atypical; comparing the set of one or more features of the first prediction to one or more other features representing historical events that are included in the time-series data input; identifying, using the comparison, a set of historical events from the historical events that are included in the time-series data input; and generating an adjusted first prediction using the identified set of historical events.

Abstract: Methods, systems, and computer-readable media that include processes related to training improved machine learning models using aggregated and unique training data. In one example, a process includes identifying a rare event for which a predicted target variable is to be generated, training a naïve machine learning model to generate the predicted target variable using a particular subset of the training data, and determining that a sparsity of training data exists for the rare event in the particular subset of training data. The process also includes the actions of, in response to determining that a sparsity off training data exists for the rare event in the particular subset of the training data, selecting other subsets of the training data, and identifying feature sets that are associated with the rare event from the selected other subsets of the training data.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request for suppliers associated with a location and a category. The program also identifies a matrix, a hierarchy of locations associated with the matrix, and a hierarchy of categories associated with the matrix. The program further determines a set of suppliers by based on the location, the category, the matrix, the hierarchy of locations, and the hierarchy of categories.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request for questions associated with a location and a category. The program also identifies a matrix, a hierarchy of locations associated with the matrix, and a hierarchy of categories associated with the matrix. The program further determines an aggregate collection of questions from a plurality of sets of questions based on the matrix, the hierarchy of locations, and the hierarchy of categories. The program also generates the aggregate collection of questions.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request for questions associated with a location and a category. The program also identifies a matrix, a hierarchy of locations associated with the matrix, and a hierarchy of categories associated with the matrix. The program further determines an aggregate collection of questions from a plurality of sets of questions based on the matrix, the hierarchy of locations, and the hierarchy of categories. The program also generates the aggregate collection of questions.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request for suppliers associated with a location and a category. The program also identifies a matrix, a hierarchy of locations associated with the matrix, and a hierarchy of categories associated with the matrix. The program further determines a set of suppliers by based on the location, the category, the matrix, the hierarchy of locations, and the hierarchy of categories.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request for suppliers associated with a location and a category. The program also identifies a matrix, a hierarchy of locations associated with the matrix, and a hierarchy of categories associated with the matrix. The program further determines a set of suppliers by based on the location, the category, the matrix, the hierarchy of locations, and the hierarchy of categories.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request for suppliers associated with a location and a category. The program also identifies a matrix, a hierarchy of locations associated with the matrix, and a hierarchy of categories associated with the matrix. The program further determines a set of suppliers by based on the location, the category, the matrix, the hierarchy of locations, and the hierarchy of categories.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a request for questions associated with a location and a category. The program also identifies a matrix, a hierarchy of locations associated with the matrix, and a hierarchy of categories associated with the matrix. The program further determines an aggregate collection of questions from a plurality of sets of questions based on the matrix, the hierarchy of locations, and the hierarchy of categories. The program also generates the aggregate collection of questions.