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Confusion Matrix

A table used to describe the performance of a classification model by showing the counts of true positives, true negatives, false positives, and false negatives.

Categoryai

Overview

The confusion matrix is the foundation for almost all classification metrics. It provides a clear breakdown of exactly where the model is making mistakes.

The Four Quadrants

  1. True Positive (TP): Predicted positive, actually positive.
  2. True Negative (TN): Predicted negative, actually negative.
  3. False Positive (FP): Predicted positive, actually negative (Type I Error).
  4. False Negative (FN): Predicted negative, actually positive (Type II Error).

Importance

It helps developers understand if a model is biased toward a certain class or if it's struggling with a specific type of error.

Related Terms

PrecisionRecallF1 ScoreROC Curve