Reference 4 stops to get here

Cohen's Kappa

A metric measuring agreement between raters/models accounting for chance agreement.

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. Accuracy ✓ understood

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

  5. Cohen's Kappa · you are here ✓ understood

A metric measuring agreement between raters/models accounting for chance agreement.

This concept is essential for understanding model evaluation & metrics and forms a key part of modern AI systems.

  • Evaluation
  • Agreement
  • Inter-Rater Reliability

Where it sits

Before this

Accuracy
Cohen's Kappa

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Evaluation Matthews Correlation Coefficient A balanced measure for binary classification considering all confusion matrix values, robust to imbalance. Evaluation Confusion Matrix A table showing true positives, true negatives, false positives, and false negatives for classification evaluation. Foundations Ground Truth The correct or true labels/values for data, used as targets during training and evaluation benchmarks.