Confusion Matrix

A confusion matrix is a table that summarizes the performance of a classification model. It displays the counts of true positive, true negative, false positive, and false negative predictions, allowing for a detailed analysis of the model's accuracy and types of errors it makes. This breakdown helps in understanding where the model excels and where it struggles, providing insights beyond simple accuracy scores.

Visit the following resources to learn more:

• @article@Confusion matrix | scikit-learn
• @article@What is A Confusion Matrix in Machine Learning? The Model Evaluation Tool Explained