Evaluation Metrics: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Evaluation Metrics = '''Evaluation Metrics''' are quantitative measures used to assess the performance of machine learning models. Choosing the right metric is essential for understanding how well a model performs, especially in classification and regression problems. == Why Are Evaluation Metrics Important? == * Provide objective criteria to compare different models. * Help detect issues like overfitting or underfitting. * Guide model improvement and selection. * R..." |
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evaluation metrics in machine learning, classification metrics, regression evaluation metrics, precision recall f1 score, roc auc explained, mean squared error, choosing evaluation metrics, model performance measures | evaluation metrics in machine learning, classification metrics, regression evaluation metrics, precision recall f1 score, roc auc explained, mean squared error, choosing evaluation metrics, model performance measures | ||
[[Category:Artificial Intelligence]] | |||