Decision making
Understand how multi-criteria methods combine evidence, criteria and preferences, and where their rankings can change.
Research field
Method family
Accuracy
Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, reg...
Adjusted R-squared
Adjusted R² is a corrected version of the coefficient of determination that accounts for the number of predictors in a regression model. Introduced by Henri Theil in 1961, it addresses the fundamental limitation of standard R²: the tendency...
Adjusted Rand Index
The Adjusted Rand Index (ARI), developed by Hubert and Arabie in 1985, is an external clustering evaluation metric that measures the agreement between a predicted clustering and a ground truth labeling. It ranges from -1 to 1, where 1 indic...
Akaike Information Criterion
The Akaike Information Criterion is an information-theoretic measure for model selection that balances goodness of fit against model complexity. Introduced by Hirotugu Akaike in 1974, AIC estimates the relative quality of models for a given...
Balanced Accuracy
Balanced accuracy is the average of recall values computed for each class separately. It corrects for class imbalance by giving equal weight to the performance on each class, regardless of class frequency in the dataset.
Brier Score
The Brier score measures the mean squared difference between predicted probabilities and actual binary outcomes. It is a simple, interpretable metric for evaluating the accuracy of probabilistic predictions, particularly in weather forecast...
Calinski-Harabasz Index
The Calinski-Harabasz Index, also called the Variance Ratio Criterion, was introduced by Calinski and Harabasz in 1974. It is a metric that measures the ratio of between-cluster variance to within-cluster variance, adjusted for the number o...
Confusion Matrix
The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calcula...
Davies-Bouldin Index
The Davies-Bouldin Index, introduced by Davies and Bouldin in 1979, is a metric for evaluating clustering quality based on the average similarity between each cluster and its most similar neighboring cluster. Lower values indicate better cl...
Dunn Index
The Dunn Index, introduced by Joseph C. Dunn in 1974, is a metric that captures cluster quality by measuring the ratio of the minimum between-cluster distance to the maximum within-cluster diameter. Higher values indicate well-separated and...
Elbow Method
The Elbow Method is a heuristic for selecting the optimal number of clusters in partitional clustering. Introduced by Robert Thorndike in 1953, it involves fitting clustering models for increasing numbers of clusters and plotting the within...
F-beta Score
The F-beta score is a weighted harmonic mean of precision and recall that allows customizing the relative importance of recall versus precision through a parameter beta. It generalizes the F1-score, which is the special case where beta = 1.
F1-Score
The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification mo...
Fowlkes-Mallows Index
The Fowlkes-Mallows Index, introduced by Fowlkes and Mallows in 1983, is an external clustering evaluation metric based on the geometric mean of precision and recall. It measures agreement between two partitions by examining pairs of points...
Gap Statistic
The Gap Statistic, developed by Tibshirani, Walther, and Hastie in 2001, is a principled statistical method for determining the optimal number of clusters in a dataset. It compares the observed within-cluster sum of squares to the expected...
Hamming Loss
Hamming loss measures the fraction of labels that are incorrectly predicted in multi-label classification. It counts the number of label mistakes divided by the total number of labels, providing a simple metric for multi-label problems.
Inertia (Within-Cluster Sum of Squares)
Inertia, also called Within-Cluster Sum of Squares (WCSS), is a measure of cluster cohesion that quantifies how tightly points are grouped around their cluster centroids. Lower values indicate more compact, cohesive clusters. Inertia is the...
Jaccard Index
The Jaccard index measures the similarity between predicted and true label sets by computing the ratio of intersection to union. It is widely used in multi-label classification and set-based similarity tasks where partial overlap is importa...
Lift and Gain Chart
Lift and gain charts visualize classifier performance by showing how much better the model performs compared to random selection, particularly useful for ranking or scoring tasks where you select a top percentage of samples. They are widely...
Log-Loss (Cross-Entropy Loss)
Log-loss measures the difference between predicted probabilities and actual labels, penalizing confident wrong predictions more than uncertain ones. It is a standard loss function in machine learning optimization and evaluates probabilistic...
Macro-averaged F1
Macro-averaged F1 computes the F1-score independently for each class and then takes the unweighted arithmetic mean. It treats all classes equally, regardless of their frequency in the dataset, making it useful for imbalanced multi-class pro...
Mean Absolute Error
Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction devi...
Mean Absolute Percentage Error
Mean Absolute Percentage Error measures prediction accuracy as a percentage relative to actual values, expressing errors in units that are scale-independent and interpretable across datasets. Formalized by J. Scott Armstrong in 1985, MAPE i...
Mean Absolute Scaled Error
Mean Absolute Scaled Error is a scale-independent metric that measures prediction accuracy relative to a simple baseline (naive forecast). Introduced by Hyndman and Koehler (2006), MASE directly compares model performance to a reference met...