Model Evaluation Metrics: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Model Evaluation Metrics = '''Model Evaluation Metrics''' are quantitative measures used to assess how well a machine learning model performs. They help determine the accuracy, reliability, and usefulness of models in solving real-world problems. == Importance of Evaluation Metrics == Without evaluation metrics, it's impossible to know whether a model is effective or not. Metrics guide model selection, tuning, and deployment by measuring: * Accuracy of predictions..." |
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model evaluation metrics, machine learning metrics, classification metrics, regression metrics, precision recall f1, accuracy in machine learning, confusion matrix explanation, roc curve importance | model evaluation metrics, machine learning metrics, classification metrics, regression metrics, precision recall f1, accuracy in machine learning, confusion matrix explanation, roc curve importance | ||
[[Category:Artificial Intelligence]] | |||