Micro AUROC
Rescaling one class’s column leaves every per-class AUROC untouched and moves micro AUROC by 0.017. Here is why.
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Micro F1 Score
The identity that makes micro precision, recall and F1 equal has one arithmetic reason — and an example that violates it cannot be real.
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Single-label Classification
Forcing one label onto overlapping data left 57.38% of rows impossible to describe correctly, and one answer recovered 13.67% of the real tags.
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Micro Recall
Per class the false positives and false negatives look nothing alike. Their totals are identical, and that is the whole mechanism.
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Micro Precision
With one true and predicted label per sample and all classes included, micro precision, recall, and F1 equal accuracy. Label subsets and multilabel data break the identity.
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One-vs-Rest (OvR) AUROC
Each OvR AUROC compares one class with its own negative mixture. Interpret the mixture and finite-sample variation before comparing class scores.
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Macro AUROC (Macro-Averaged AUROC)
Macro AUROC weights classes equally, but OvR still depends on the negative-class mixture. OvO, uncertainty, and thresholds need separate interpretation.
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Macro F1
Averaging then combining and combining then averaging give different answers. Which one your report contains is worth knowing.
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Macro Recall
Macro recall and unadjusted balanced accuracy match on the same present classes. Absent labels and zero-division conventions can change that comparison.
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Macro Precision
The same predictions can score 0.27, 0.81 or 0.94 macro precision. What changed was zero_division, not the model.
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Multiclass stratified CV
A class with three samples cannot appear in five folds. scikit-learn warns instead of failing, and the warning is easy to miss.
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Micro AUC
In single-label problems the pooled positive rate is 1/K, providing a no-skill reference for micro average precision rather than a guaranteed floor.
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Macro AUC
Three classes with similar ROC-AUC score 0.96, 0.59 and 0.23 on average precision. Prevalence changes how these metrics should be read.
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Per-Class Precision
Reading the per-class table alongside support and predicted counts, and why a rare class can score 0.18 precision at 0.41 recall.