AUC-ROC
AUC (Area Under the Curve), most often the area under the ROC curve (AUC-ROC), is a threshold-independent evaluation metric that measures how well a binary classification model ranks positive cases above…
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Area under the curve
Area under the curve (AUC) is a single scalar metric that summarizes the performance of a binary classifier or diagnostic test across all possible decision thresholds by integrating the area beneath a…
Elo rating system (AI model ranking)
The Elo rating system, as applied to AI models, is a method for turning a pile of head-to-head preference votes into a single number per model, so that large language models and chatbots can be ranked on a…
Expected calibration error
Expected calibration error (ECE) is a metric that measures how well a classifier's predicted confidence matches its observed accuracy.
F1 score
The F1 score (also written as F1-score, F-score, or F-measure) is the harmonic mean of precision and recall, calculated as $$F_1 = \frac{2 \cdot (\text{Precision} \cdot \text{Recall})}{\text{Precision} +…
False Negative Rate
The false negative rate (FNR), also known as the miss rate, is the proportion of actual positive instances that a model or test incorrectly classifies as negative, computed as FNR = FN / (FN + TP).
False Positive Rate (FPR)
The false positive rate (FPR) is the proportion of actual negative cases that a test, model, or decision process incorrectly classifies as positive, defined as FPR = FP / (FP + TN) where FP is the number of…
False negative
A false negative (FN), also called a Type II error or a miss, is an instance whose true label is positive but that a classification model or test predicts as negative: a real positive case that the model fails…
False positive
A false positive (FP), also called a Type I error or a false alarm, is an instance whose true label is negative but whose predicted label is positive: the classifier raises an alarm where none was warranted.
Inter-rater agreement
Inter-rater agreement is the degree of consensus among two or more independent raters when they label or score the same set of items.
Mean Absolute Error (MAE)
Mean Absolute Error (MAE) is a regression accuracy metric and loss function that measures the average absolute difference between predicted values and actual observed values
Mean Squared Error (MSE)
Mean Squared Error (MSE), also called mean squared deviation (MSD), is the average of the squared differences between predicted values and actual (observed) values
Root Mean Squared Error (RMSE)
Root Mean Squared Error (RMSE), also known as root mean square deviation (RMSD), is a regression evaluation metric equal to the square root of the average of the squared differences between predicted and…
True negative
A true negative (TN) is a case that a binary classification model correctly predicts as belonging to the negative class: the true label is negative and the predicted label is also negative.
True positive
A true positive (TP) is a prediction that is correctly positive: the model predicts the positive class and the true label is also positive.