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Model Evaluation Metrics
Model evaluation metrics are quantitative measures used to assess how well an AI model performs on a given task. These metrics, such as accuracy, precision, recall, or F1-score, provide objective ways to compare different models or track the improvement of a single model over time. The choice of metric often depends on the specific goals and characteristics of the problem being solved.
In plain terms
Model evaluation metrics are like a report card for an AI model, showing its grades in different subjects.
Why it matters
They allow developers to objectively understand a model's strengths and weaknesses, guiding decisions about further development and deployment.
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