F1 Score
The F1 score is a single classification metric that combines precision and recall into one number by calculating their harmonic mean, providing a balanced measure of a model's performance when both false positives and false negatives matter.
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Glossary Terms(4)
Confusion Matrix
A confusion matrix is a table that summarizes the performance of a classification model by showing the counts of correct and incorrect predictions broken down…
Precision and Recall
Precision and recall are two complementary metrics for evaluating a classification model: precision measures how many of the model's positive predictions were…
F1 Score
The F1 score is a single classification metric that combines precision and recall into one number by calculating their harmonic mean, providing a balanced meas…
ROC Curve
A ROC (Receiver Operating Characteristic) curve is a graph that plots a binary classification model's true positive rate against its false positive rate across…