Importance

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Abstract

There is provided a method and system for providing a recommendation for a given problem by using a set of supervised machine learning (ML) models online by performing dynamic model evaluation and selection. An optional knowledge capture phase may be used to train the set of ML models offline using passive and/or active learning. Upon detection of a suitable initialization condition, the set of ML models is provided for inference and a feature vector is obtained. A set of predictions associated with accuracy metrics is generated by the set of models based on the feature vector. The accuracy metric may be global or class-specific. A recommendation is provided based on at least one of the set of predictions. The recommendation may be provided by selecting a best model, or by performing a vote weighted by the accuracy metrics. The set of ML models is retrained after obtaining an actual prediction.

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