Landmark 4 stops to get here · leads to 2

Precision

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

Your route here

4 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 · you are here ✓ understood

Picture it

PREDICTEDpositivenegativeACTUALpositivenegativeTP42FN6FP8TN44precision = TP / (TP + FP)= 42 / 50 = 0.84recall = TP / (TP + FN)= 42 / 48 = 0.88
Notice precision only looks at the predicted-positive column: of everything flagged positive, TP/(TP+FP) were actually right.

Where it sits

Before this

Confusion Matrix
Precision

Leads to

F1 Score Precision-Recall Curve

Explore nearby

Evaluation Recall The proportion of true positives among all actual positives - measures how many actual positives were correctly identified. Evaluation F1 Score The harmonic mean of precision and recall, providing a single metric that balances both concerns. Evaluation Accuracy The proportion of correct predictions out of total predictions, a basic classification metric. Evaluation Precision-Recall Curve A curve showing the tradeoff between precision and recall at different thresholds. Evaluation False Positive Incorrectly predicted positive cases (Type I error) in classification.