Model evaluation parameters (metrics)

Model evaluation metrics are needed to measure model efficiency. Choosing assessment metrics depends on a particular task of ML (such as classification, regression, ranking, clustering, or subject modeling, among others). Certain metrics, such as precision recall, are useful for multiple tasks. Supervised- and experience (historical data)-based ML models, such as classification and regression, form the majority of applications for ML. 

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