Categorical Prediction Task Performance Measure

A Categorical Prediction Task Performance Measure is a prediction task performance measure for categorical prediction tasks that evaluates classification accuracy and classification errors.

• AKA: Classification Performance Measure, Classification Task Performance Measure, Classifier Performance Metric, Classification Evaluation Measure, Categorical Classification Metric, Classification Model Performance Measure.
• Context:
  • It can (typically) evaluate True Positives, True Negatives, False Positives, and False Negatives from confusion matrices.
  • It can (typically) assess Class Imbalance effects on classification performance.
  • It can (typically) incorporate Misclassification Costs when errors have different consequences.
  • It can (often) require Threshold Selection for probabilistic classifiers.
  • It can (often) be computed using Cross-Validation or Hold-Out Validation for robust estimation.
  • It can (often) guide Model Selection and Hyperparameter Tuning in machine learning pipelines.
  • It can (often) be aggregated across classes using Macro-Averaging, Micro-Averaging, or Weighted Averaging.
  • It can range from being a Binary Classification Performance Measure to being a Multi-Class Classification Performance Measure, depending on its class count.
  • It can range from being a Threshold-Dependent Classification Measure to being a Threshold-Independent Classification Measure, depending on its decision boundary sensitivity.
  • It can range from being a Class-Specific Performance Measure to being an Overall Performance Measure, depending on its aggregation scope.
  • It can range from being a Point Estimate Classification Measure to being a Probabilistic Classification Measure, depending on its prediction type.
  • It can integrate with Machine Learning Frameworks for automated evaluation.
  • It can integrate with Model Monitoring Systems for production deployment.
  • ...
• Example(s):
  • Accuracy-Based Measures, such as:
    • Classification Accuracy Measure: Proportion of correct predictions overall.
    • Balanced Accuracy: Average of recall obtained on each class.
    • Top-K Accuracy: Whether true class is in top K predictions.
  • Error-Based Measures, such as:
    • Classification Error Rate: Proportion of incorrect predictions.
    • Mean Per-Class Error: Average error rate across classes.
    • Confusion Matrix: Complete error breakdown by class.
  • Precision-Recall Measures, such as:
    • Precision Measure: Proportion of positive predictions that are correct.
    • Recall Measure (Sensitivity): Proportion of actual positives correctly identified.
    • F1-Score: Harmonic mean of precision and recall.
    • F-Beta Score: Weighted harmonic mean with beta parameter.
    • Macro-F1 Measure: F1 averaged across all classes.
    • Micro-F1 Measure: F1 computed globally across all instances.
  • Threshold-Independent Measures, such as:
    • Area Under ROC Curve (AUC-ROC): Performance across all thresholds.
    • Area Under Precision-Recall Curve (AUC-PR): Performance on imbalanced data.
    • Average Precision (AP): Weighted mean of precisions at each threshold.
  • Correlation-Based Measures, such as:
    • Matthews Correlation Coefficient (MCC): Correlation between predicted and actual.
    • Cohen's Kappa: Agreement corrected for chance.
    • Phi Coefficient: Association for binary classification.
  • Probabilistic Measures, such as:
    • Log Loss (Cross-Entropy): Logarithmic penalty for wrong predictions.
    • Brier Score: Mean squared difference between predicted probabilities and outcomes.
    • Calibration Error: Difference between predicted and observed probabilities.
  • Cost-Sensitive Measures, such as:
    • Expected Cost: Weighted sum of misclassification costs.
    • Profit Curve: Profit as function of classification threshold.
    • Cost Curve: Expected cost across class distributions.
  • Multi-Class Specific Measures, such as:
    • Macro-Precision: Average precision across all classes.
    • Micro-Recall: Global recall across all instances.
    • Weighted F-Score: F-score weighted by class frequency.
    • One-vs-All AUC: AUC for each class against all others.
  • Imbalanced Data Measures, such as:
    • G-Mean: Geometric mean of sensitivity and specificity.
    • Informedness (Youden's J): Sensitivity + Specificity - 1.
    • Markedness: Precision + Negative Predictive Value - 1.
  • Information-Theoretic Measures, such as:
    • Mutual Information: Information shared between predictions and labels.
    • Normalized Mutual Information: Normalized version for comparison.
    • Adjusted Rand Index: Rand index corrected for chance.
  • ...
• Counter-Example(s):
  • Regression Performance Measure, which evaluates continuous predictions rather than categories.
  • Ranking Performance Measure, which evaluates ordering rather than classification.
  • Clustering Performance Measure, which evaluates unsupervised grouping.
  • Ordinal Prediction Task Performance Measure, which considers ordering of categories.
  • Sequence Labeling Performance Measure, which evaluates sequential predictions.
  • Object Detection Performance Measure, which combines localization and classification.
• See: Classification Task, Confusion Matrix, ROC Curve, Precision-Recall Curve, Class Imbalance, Cost-Sensitive Learning, Threshold Selection, Model Calibration, Binary Classification, Multi-Class Classification, Multi-Label Classification, Imbalanced Learning, Performance Evaluation, Cross-Validation, Model Selection.

---

---

---