Building A Confusion Matrix Step By Step
This animation walks through a set of model predictions versus true labels, sorting each case into true positive, false positive, true negative, or false negative before placing it into a growing confusion matrix grid. Viewers see how raw prediction data becomes a structured table, setting up the calculation of accuracy, precision, recall, and F1 score. Useful for students and teachers introducing classification evaluation in statistics or machine learning courses.
Narrated · 16:9 · every frame verified for overlaps, spacing and edges before rendering
The prompt that made it
Read and construct a confusion matrix from a set of predictions
Make the next one in this series
- Show how precision and recall are calculated directly from a confusion matrix
- Compare two classifiers using ROC curves built from their confusion matrices
- Explain how class imbalance distorts accuracy using confusion matrix examples
