What are some common performance metrics used in evaluating classification models?
- Clustering Coefficient, Density
- Eigenvalues, Eigenvectors
- Mean Squared Error, R-squared
- Precision, Recall, F1 Score
Common performance metrics for classification include Precision (positive predictive value), Recall (sensitivity), and F1 Score (harmonic mean of precision and recall). These metrics help to assess the model's ability to correctly classify positive cases.
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