Reference collection
Model quality & fit
Interpret model-fit and prediction metrics while separating relative comparisons from defensible thresholds.
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Interpretation guide
Silhouette Score, Brier Score, and Calibration: What Counts as Good?
Interpret silhouette values, Brier scores, calibration plots, and log loss without treating weakly sourced or task-dependent thresholds as universal.
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Interpretation guide
What Is a Good R-Squared Value? Why Context Matters
Interpret an R-squared value without relying on a universal cutoff. Compare model purpose, field expectations, residuals, and out-of-sample evidence.
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Interpretation guide
r vs R-Squared vs Adjusted R-Squared: Which Result Do You Have?
Tell correlation r, R-squared, and adjusted R-squared apart by range, output location, meaning, and response to additional predictors.
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Interpretation guide
AUC and ROC Curve Interpretation: What Counts as Good?
Interpret AUC and ROC curves with attributed reference bands, then check class balance, uncertainty, threshold tradeoffs, and calibration.
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Interpretation guide
RMSE vs MAE vs MAPE: How to Interpret Error Without a Universal Cutoff
Compare RMSE, MAE, MSE, and MAPE, understand their units and failure modes, and judge error against a baseline instead of a universal cutoff.
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Interpretation guide
AIC vs BIC: How to Compare Models Without an Absolute Cutoff
Compare AIC and BIC as relative model criteria, understand their different penalties, and interpret differences only within a valid candidate set.
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Interpretation guide
F1 Score Interpretation: What Counts as Good?
Interpret F1 as a task-dependent balance of precision and recall, then distinguish binary, macro, micro, and weighted averages without a universal cutoff.