ML model evaluation

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ML-R1.7-02M: Metadata describes ML model evaluation. Test: 1) The result of the learning process is explained and described.

Principle: R

Rationale: Ml model evaluation should be described with all notable indicators, such as confusion matrix, F1 score, Area Under the ROC Curve, etc. The indicators should demonstrate and describe the quality and performance of the model. This is a disciplinary requirement.

FAIR Metrics: R1.7

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Target Rubric Project   ML model evaluation