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Machine Learning Easy Theory

Precision vs recall vs F1

Precision asks how many predicted positives were right, recall asks how many real positives were found, and F1 balances both.

  • Precision avoids false positives
  • Recall avoids false negatives
  • F1 is harmonic mean
#classification #metrics #ml

Precision vs recall vs F1

Machine Learning Medium Theory

What is ROC-AUC?

ROC-AUC measures how well a classifier ranks positives ahead of negatives across decision thresholds.

  • Threshold-independent ranking view
  • Useful for comparing classifiers
  • Can mislead on highly imbalanced data
#classification #metrics #ml

What is ROC-AUC?