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 actually correct, while recall measures how many of the actual positive cases the model successfully found.
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Glossary Terms(5)
Model Drift
Model drift (also called concept drift) is the gradual decline in a machine learning model's predictive performance over time, occurring because the real-world…
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…