Method and system for model auto-selection using an ensemble of machine learning models
AssigneeKinaxis Inc.
InventorsKanchana Padmanabhan, Brian Keng
Life of this patent
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Abstract
A system and method for model auto-selection for a prediction using an ensemble of machine learning models. The method includes: receiving historical data, the historical data including previous outcomes of a plurality of events associated with a plurality of data categories; training candidate machine learning models with the historical data, each candidate machine learning model trained using a respective one of the data categories; and determining an ensemble of machine learning models by determining a median prediction for combinations of candidate machine learning models and determining the combination that has the median prediction that is closest to at least one of the previous outcomes.
Claims17
A system for automated model selection for predictive analytics using an ensemble of machine learning models, comprising:
- one or more processors operatively coupled to a memory;
- a component executable by the one or more processors and configured to receive historical data comprising previous outcomes of a plurality of events associated with a plurality of data categories;
- a component executable by the one or more processors and configured to train a plurality of candidate machine learning models using the historical data, each candidate machine learning model trained using a respective one of the data categories;
- a component executable by the one or more processors and configured, for each data category, to:
- obtain a respective prediction from each of the candidate machine learning models for that data category;
- determine a plurality of possible combinations of the candidate machine learning models;
- generate a prediction for each combination based on the predictions of the models in that combination;
- and
- select one of the combinations having a prediction that is closest to the respective previous outcomes as a respective ensemble of machine learning models for the data category;
- and
- a component executable by the one or more processors and configured to output the respective ensemble of models for each of the plurality of data categories.
A method for model auto-selection for a prediction using an ensemble of machine learning models, the method executed on at least one processing unit, the method comprising:
- receiving historical data, the historical data comprising previous outcomes of a plurality of events associated with a plurality of data categories;
- training candidate machine learning models with the historical data, each candidate machine learning model trained using a respective one of the data categories, wherein training the candidate machine learning models comprises training at least two of the models in parallel;
- for each data category of the plurality of data categories:
- obtaining a respective prediction from each of the candidate machine learning models for that respective data category;
- determining a respective plurality of possible combinations of the candidate models for that respective data category;
- determining, for each of the plurality of respective possible combinations of the candidate models, a prediction based on the predictions of each of the candidate machine learning models in that respective combination;
- and
- determining one of the plurality of possible combinations of the candidate models having a prediction that is closest to the respective previous outcomes as a respective ensemble of machine learning models for the data category;
- and
- outputting the respective ensemble of models for the respective data category for each of the plurality of data categories.
Dependencies are inferred from the claim text.