AUCScore
The AUC score, or Area Under the ROC Curve, is a classification evaluation metric that measures how well a model distinguishes between positive and negative classes across all possible decision thresholds.
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Glossary Terms(4)
Cross-Entropy Loss
Cross-entropy loss is a loss function that measures the difference between a predicted probability distribution and the true (target) distribution, commonly us…
Mean Squared Error
Mean squared error (MSE) is a loss function that measures the average of the squared differences between predicted and actual values, commonly used to train an…
AUC Score
The AUC score, or Area Under the ROC Curve, is a classification evaluation metric that measures how well a model distinguishes between positive and negative cl…
A/B Testing (ML)
A/B testing in machine learning is a controlled experimentation method that compares two or more model versions by routing production traffic between them and…