Classification Diagnostics

A reading lens for confusion matrices. Aggregate accuracy tells you a classifier works. The confusion matrix tells you what it does not know yet.

When to use

• First look at a trained classifier's validation performance.
• Between training runs to decide what to change next.
• Before shipping a model: verifying that no critical class is failing silently.
• Debugging a production regression where overall accuracy dropped one point and you need to know why.

Inputs

• cm: CxC confusion matrix (rows = true, cols = predicted).
• labels: list of C class names, in the same order.
• Optional class_priors: per-class training frequency (defaults to the row sums of cm).

Steps

1. Compute per-class metrics. Treat any division by zero as the metric being undefined for that class, and report it as n/a; never substitute silently with 0.

   • precision_i = cm[i,i] / sum(cm[:, i]) (undefined when the class was never predicted)
   • recall_i = cm[i,i] / sum(cm[i, :]) (undefined when the class has no ground-truth samples)
   • f1_i = 2 * p * r / (p + r) (undefined when either component is undefined)

2. Rank up to three worst classes by F1. If the confusion matrix has fewer than three classes, rank however many exist. Exclude classes with all metrics undefined.

3. Find the top off-diagonal cell per row — the one class that most commonly steals from this class. Report as true -> predicted.

4. Classify the failure mode for each worst class. Use these quantitative thresholds so the label is reproducible:

   • ambiguity — bidirectional confusion with another class: both cm[i,j] / sum(cm[i, :]) >= 0.15 and cm[j,i] / sum(cm[j, :]) >= 0.15.
   • imbalance — the class has < 0.5x the training count of its top confuser.
   • label_noise — |precision_i - recall_i| >= 0.2 and the class is not on the imbalance / ambiguity paths.
   • systematic — no single confuser exceeds 0.2 share of this class's errors; errors spread across three or more other classes.

5. Recommend the single most impactful next action:

   • ambiguity -> collect or synthesise discriminative examples, add targeted augmentation that preserves the distinguishing feature.
   • imbalance -> oversample the minority class or apply class-weighted loss.
   • label_noise -> audit a stratified sample of the class; fix mislabels before any other change.
   • systematic -> increase data for the class or fine-tune with a higher weight on this class's loss.

Report

[diagnostics]
  aggregate accuracy: X.XX
  macro F1:           X.XX

[top-3 worst classes]
  1. class <name>  F1 = X.XX  prec = X.XX  rec = X.XX
     top confusion: <name> -> <other>  (N cases)
     failure mode:  ambiguity | imbalance | label_noise | systematic
     action:        <one sentence>

  2. ...
  3. ...

[recommendation]
  single biggest lever: <one sentence naming the class and the fix>

Rules

• Return at most three classes. More hides the signal.
• Name the dominant confuser for each worst class; never summarise as "confuses with many".
• Ground every recommendation in the confusion matrix evidence. No generic "add more data" without specifying which class.
• When precision and recall disagree by more than 0.2, always flag label noise as a candidate — real classes usually have aligned P and R after training.