Landmark 3 stops to get here · leads to 1

Accuracy

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

Your route here

3 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. Accuracy · you are here ✓ understood

Picture it

PREDICTEDpositivenegativeACTUALpositivenegativeTP42FN6FP8TN44precision = TP / (TP + FP)= 42 / 50 = 0.84recall = TP / (TP + FN)= 42 / 48 = 0.88
Accuracy is (TP + TN) over all four cells; notice how precision and recall below each look at only part of the matrix.

Where it sits

Before this

Classification
Accuracy

Leads to

Cohen's Kappa

Explore nearby

Evaluation Precision The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive. 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 Confusion Matrix A table showing true positives, true negatives, false positives, and false negatives for classification evaluation. Foundations Imbalanced Dataset A dataset where classes have significantly different numbers of examples, causing models to bias toward majority classes.