Evaluatie & betrouwbaarheid
73 methoden in deze familie.
Uitgelicht
NauwkeurigheidAccuracy 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 howGecorrigeerde R² (R²_adj)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 addreAangepaste Rand IndexThe 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 Akaike Informatiecriterium (AIC)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 19Gebalanceerde nauwkeurigheidBalanced 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, regarBrier ScoreThe 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
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Alle methoden 73
NauwkeurigheidGecorrigeerde R² (R²_adj)Aangepaste Rand IndexAkaike Informatiecriterium (AIC)Gebalanceerde nauwkeurigheidBrier ScoreBody Shape Questionnaire (BSQ)Calinski-Harabasz IndexKalorimeterkalibratieItemanalyse van geautomatiseerde adaptieve toetsenVerwarringsmatrixContrafactische VerklaringenDavies-Bouldin IndexDunn IndexElbow-methodeExplainable Association RulesUitlegbare Autoencoder AnomaliedetectieUitlegbare BeslisboomExplainable FP-GrowthExplainable Gaussian Mixture ModelUitlegbare Gaussische ProcessenUitlegbare HDBSCANUitlegbare Isolation ForestExplainable K-MeansUitlegbare K-Nearest NeighborsExplainable LightGBMVerklaarbare Naive BayesExplainable One-Class SVMUitlegbare Random ForestExplainable Stacking EnsembleUitlegbare Support Vector MachineUitlegbare Stem-EnsembleExplainable XGBoostF-beta ScoreF1-scoreFairness-Aware Machine LearningFowlkes-Mallows IndexGap StatisticGeometrische morfometrieGlaucoma Quality of Life-15Hamming-verliesTraagheidJaccard IndexLift- en gain-grafiekenLIME: Lokaal interpreteerbare modelonafhankelijke verklaringenLog-verlies (Cross-Entropy Loss)Longitudinale itemanalyseMacro-gemiddelde F1Gemiddelde Absolute Fout (MAE)Gemiddelde Absolute Percentage Fout (MAPE)Mean Absolute Scaled Error (MASE)Gemiddelde Kwadratische Fout (MSE)Micro-gemiddelde F1ModelkalibratieGenormaliseerde Mutuele InformatiePrecisiePrecisie-Recall AUCPrijsbillijkheidsschaalR-kwadraat (R²)Gevoeligheid (Recall)Robuuste Rasch ModelRoot Mean Squared Error (RMSE)SHAP (SHapley Additive exPlanations)Short Form Rasch ModelShort-Form Item Response Theory (SF-IRT)Silhouette ScoreSpecificiteitSurveyweging en KalibratieSymmetrische MAPE (sMAPE)Token Bucket Rate Limiting AlgorithmV-measureGewogen F1Youdens J-statistiek