Standard 6 stops to get here · leads to 1

F1 Score

The harmonic mean of precision and recall, providing a single metric that balances both concerns.

Your route here

6 stops · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Supervised Learning ✓ understood

    Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.

  3. Classification ✓ understood

    A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.

  4. Confusion Matrix ✓ understood

    A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.

  5. Precision ✓ understood

    Of everything a model flagged as positive, the fraction that really was positive: TP / (TP + FP). It answers "when the model says yes, how often is it right?"

  6. Recall ✓ understood

    The share of actual positives a model correctly identifies, TP / (TP + FN); also called sensitivity or the true positive rate.

  7. F1 Score · you are here ✓ understood

Where it sits

Before this

Precision Recall
F1 Score

Leads to

Dice Coefficient

Explore nearby

Evaluation Accuracy The share of predictions a classifier gets right. Simple and intuitive, but misleading when one class is much rarer than the others. Evaluation Matthews Correlation Coefficient A balanced measure for binary classification considering all confusion matrix values, robust to imbalance. Evaluation Precision-Recall Curve A curve showing the tradeoff between precision and recall at different thresholds. Evaluation Confusion Matrix A table showing true positives, true negatives, false positives, and false negatives for classification evaluation.