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

    The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive.

  6. Recall ✓ understood

    The proportion of true positives among all actual positives - measures how many actual positives were correctly identified.

  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 proportion of correct predictions out of total predictions, a basic classification metric. 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.