📊 COMPLETE NUMBERED LIST OF ML EVALUATION METRICS
🔵 A) REGRESSION METRICS (6)
1️⃣ Mean Squared Error (MSE)
2️⃣ Root Mean Squared Error (RMSE)
3️⃣ Mean Absolute Error (MAE)
4️⃣ R-Squared (R²)
5️⃣ Mean Absolute Percentage Error (MAPE)
6️⃣ Mean Bias Error (MBE)
🔴 B) CLASSIFICATION METRICS (9)
7️⃣ Accuracy
8️⃣ Precision
9️⃣ Recall (Sensitivity)
🔟 F1 Score
1️⃣1️⃣ ROC-AUC
1️⃣2️⃣ Precision-Recall AUC (AUC-PR)
1️⃣3️⃣ Matthews Correlation Coefficient (MCC)
1️⃣4️⃣ Log Loss (Cross Entropy)
1️⃣5️⃣ Cohen’s Kappa
🟢 C) CLUSTERING METRICS (6)
1️⃣6️⃣ Silhouette Score
1️⃣7️⃣ Davies-Bouldin Index (DBI)
1️⃣8️⃣ Calinski-Harabasz Index
1️⃣9️⃣ Adjusted Rand Index (ARI)
2️⃣0️⃣ Dunn Index
2️⃣1️⃣ Mutual Information (MI)
🟣 D) NLP METRICS (6)
2️⃣2️⃣ BLEU
2️⃣3️⃣ ROUGE
2️⃣4️⃣ METEOR
2️⃣5️⃣ BERTScore
2️⃣6️⃣ Perplexity (PPL)
2️⃣7️⃣ Word Error Rate (WER)
🟡 E) RECOMMENDATION SYSTEM METRICS (6)
2️⃣8️⃣ MAP@K (Mean Average Precision at K)
2️⃣9️⃣ NDCG (Normalized Discounted Cumulative Gain)
3️⃣0️⃣ Coverage (Catalog & Prediction)
3️⃣1️⃣ MRR (Mean Reciprocal Rank)
3️⃣2️⃣ Diversity / Intra-list Similarity
3️⃣3️⃣ Semantic Coverage
🟠 F) COMPUTER VISION METRICS (4)
3️⃣4️⃣ mAP (Mean Average Precision)
3️⃣5️⃣ Intersection over Union (IoU)
3️⃣6️⃣ Dice Coefficient
3️⃣7️⃣ Hausdorff Distance
🟤 G) FAIRNESS & ETHICS METRICS (4)
3️⃣8️⃣ Demographic Parity
3️⃣9️⃣ Equalized Odds
4️⃣0️⃣ Bounded Group Loss
4️⃣1️⃣ Construct Validity
✅ TOTAL METRICS COUNT:
👉 41 Core Evaluation Metrics
