Abstract: Systems and methods for frequent machine learning model retraining and rule optimization are disclosed. In accordance with aspects, a method may include generating a challenger machine learning model based on a production machine learning model; training the challenger machine learning model on a plurality of datasets; scoring historical data with the challenger machine learning model, wherein the scoring produces a respective score for each record of a plurality of records in the historical data; determining that the challenger model performs within predetermined thresholds based on the scoring; selecting an optimal scaler value for a rule based on execution of the rule with a range of scaler values applied to the respective score for each record of the plurality of records evaluated by the rule; determining that the optimal scaler value outperforms a production scaler value; and promoting the challenger model and the optimal scaler value to a production environment.

Abstract: Systems and methods for rule optimization are disclosed. In accordance with aspects, a method may include providing a segment rule, where the segment rule uses a machine learning model score associated with a data record and a scaler value to evaluate data records and the segment rule is configured to evaluate data records categorized into a corresponding segment of the segment rule by attributes of the data records; receiving, at the segment rule, a categorized set of data records, wherein each data record in the categorized set of data records is categorized in the corresponding segment of the segment rule based on attributes of the data records; iteratively evaluating, by the segment rule, each received data record with a range of scaler values, where the iteratively evaluating produces a plurality of outputs of the segment rule; and determining an optimal output from the plurality of outputs.

Abstract: Systems and methods for frequent machine learning model retraining and rule optimization are disclosed. In accordance with aspects, a method may include generating a challenger machine learning model based on a production machine learning model; training the challenger machine learning model on a plurality of datasets; scoring historical data with the challenger machine learning model, wherein the scoring produces a respective score for each record of a plurality of records in the historical data; determining that the challenger model performs within predetermined thresholds based on the scoring; selecting an optimal scaler value for a rule based on execution of the rule with a range of scaler values applied to the respective score for each record of the plurality of records evaluated by the rule; determining that the optimal scaler value outperforms a production scaler value; and promoting the challenger model and the optimal scaler value to a production environment.

Abstract: Systems and methods for frequent machine learning model retraining and rule optimization are disclosed. In accordance with aspects, a method may include retrieving, from a data store, a plurality of datasets; generating a challenger machine learning model, wherein the challenger machine learning model is generated from a production machine learning model, and includes variables and variable weights included in the production machine learning model; training the challenger machine learning model with the plurality of datasets; adjusting the variable weights of the challenger machine learning model based on patterns in the plurality of datasets determined by the challenger machine learning model; performing a comparative analysis between the challenger model and the production model; and promoting the challenger model to a production environment based on the comparative analysis.