Abstract: Computer-based models can be developed, deployed, and managed in an automated manner. For example, a model building tool can be selected based on the model building tool being compatible with one or more parameters. A first machine-learning model can be generated using the model building tool and trained using a training dataset. The first machine-learning model can then be used to perform a task. Thereafter, a new model-building tool can be selected based on the new model-building tool being compatible with the one or more parameters. A second machine-learning model can be generated using the new model-building tool and trained using the training dataset. The accuracy of the first machine-learning model can be compared to the accuracy of the second machine-learning model. Based on the second machine-learning model being more accurate, the second machine-learning model can be used to perform the particular task rather than the first machine-learning model.

Abstract: Computer-based models can be developed, deployed, and managed in an automated manner. For example, a model building tool can be selected based on the model building tool being compatible with one or more parameters. A first machine-learning model can be generated using the model building tool and trained using a training dataset. The first machine-learning model can then be used to perform a task. Thereafter, a new model-building tool can be selected based on the new model-building tool being compatible with the one or more parameters. A second machine-learning model can be generated using the new model-building tool and trained using the training dataset. The accuracy of the first machine-learning model can be compared to the accuracy of the second machine-learning model. Based on the second machine-learning model being more accurate, the second machine-learning model can be used to perform the particular task rather than the first machine-learning model.

Abstract: Computer-based models can be developed, deployed, and managed in an automated manner. For example, a model building tool can be selected based on the model building tool being compatible with one or more parameters. A first machine-learning model can be generated using the model building tool and trained using a training dataset. The first machine-learning model can then be used to perform a task. Thereafter, a new model-building tool can be selected based on the new model-building tool being compatible with the one or more parameters. A second machine-learning model can be generated using the new model-building tool and trained using the training dataset. The accuracy of the first machine-learning model can be compared to the accuracy of the second machine-learning model. Based on the second machine-learning model being more accurate, the second machine-learning model can be used to perform the particular task rather than the first machine-learning model.

Abstract: Computer-based models can be developed, deployed, and managed in an automated manner. For example, a model building tool can be selected based on the model building tool being compatible with one or more parameters. A first machine-learning model can be generated using the model building tool and trained using a training dataset. The first machine-learning model can then be used to perform a task. Thereafter, a new model-building tool can be selected based on the new model-building tool being compatible with the one or more parameters. A second machine-learning model can be generated using the new model-building tool and trained using the training dataset. The accuracy of the first machine-learning model can be compared to the accuracy of the second machine-learning model. Based on the second machine-learning model being more accurate, the second machine-learning model can be used to perform the particular task rather than the first machine-learning model.

Abstract: Computer-implemented systems and methods for updating champion predictive models that operate within a production environment. A system and method can include evaluating the predictive model performance of a champion predictive model. Based upon an indication of decay of predictive model performance of the champion predictive model, a corrective action is performed to correct the performance of the champion predictive model.

Abstract: A computer-implemented dimension engine that automatically identifies the market segments represented in user-specified input data. The dimension engine creates new dimension variables based on those segments that most accurately predict the outcomes of a target variable. A data store is used to store the input data. A decision tree processing module determines a subset of the dimension variables to split the input data. The splitting of the dimension variables predicts the target variable. A multi-dimension viewer generates a report using the determined dimension variables subset and the splitting of the dimension variables.

Abstract: A model repository is provided for storing selected data models generated by a data mining application. The model repository may include one or more index data structures for storing attributes of the models within the model repository. A user may then search through the one or more indexes in order to find a model that suits his or her needs.

Abstract: A model repository is provided for storing selected data models generated by a data mining application. Associated with the model repository is a model repository facility that is preferably integrated into the data mining application and enables operations, such as the exportation of useful models to the model repository. The model repository may also include one or more searchable index data structures for storing attributes of the models within the model repository. These indexes may include a main index that contains attributes of all the models stored in the model repository, and one or more special indexes, such as a tree-type index and mini-index, that contain the attributes of a particular sub-set of the models stored in the model repository.

Abstract: Computer-implemented systems and methods for updating champion predictive models that operate within a production environment. A system and method can include evaluating the predictive model performance of a champion predictive model. Based upon an indication of decay of predictive model performance of the champion predictive model, a corrective action is performed to correct the performance of the champion predictive model.

Abstract: A computer-implemented knowledge repository data interface system and method for use by client applications to interact with a plurality of knowledge repositories. The knowledge repositories contain analytical models of interest to the client applications. A request handling module receives requests regarding the models from one of the client applications over a network. Knowledge repository application programming interfaces (APIs) are used to retrieve data about the models in the knowledge repositories based upon the received requests.

Abstract: A model repository is provided for storing selected data models generated by a data mining application. The model repository is a structure that may be organized into a plurality of levels, including a project level, a diagram level, and a model level. The project level may include one or more diagrams, each of which describes a particular set of model specifications. Each diagram may then be associated with one or more models. Associated with the model repository is a model repository facility that is preferably integrated into the data mining application and enables operations, such as the exportation of useful models to the model repository. The model repository may also include one or more index data structures for storing attributes of the models within the model repository.

Abstract: A computer-implemented knowledge repository data interface system and method for use by client applications to interact with a plurality of knowledge repositories. The knowledge repositories contain analytical models of interest to the client applications. A request handling module receives requests regarding the models from one of the client applications over a network. Knowledge repository application programming interfaces (APIs) are used to retrieve data about the models in the knowledge repositories based upon the received requests.

Abstract: A computer-implemented dimension engine that automatically identifies the market segments represented in user-specified input data. The dimension engine creates new dimension variables based on those segments that most accurately predict the outcomes of a target variable. A data store is used to store the input data. A decision tree processing module determines a subset of the dimension variables to split the input data. The splitting of the dimension variables predicts the target variable. A multi-dimension viewer generates a report using the determined dimension variables subset and the splitting of the dimension variables.