Can you extract the unique metrics for multiclass classification

Evaluation Metrics Used

Accuracy
False Positive Rate (FPR)
F1 Score
Mean Squared Error (MSE)
Mean Absolute Error (MAE)
Computational Cost
Memory Efficiency
Detection Time
Reward Optimization in Reinforcement Learning Approaches
Accuracy
Detection rate
Communication overhead
Sending rate
Latency
Throughput
Precision
Feature reduction efficiency
True Positive Rate (TPR)
True Negative Rate (TNR)
False Negative Rate (FNR)
F1 Score
ROC Curve Analysis
F1 Score (Lifecycle detection: 0.994)
False-Positive Rate
Error Rate
Detection Accuracy (AD)
Training Time & Test Time
Recall (FANET: 93.87)
Specificity
Storage Space Reduction (86.9% for FI method)
Inverted Kolmogorov-Smirnov D statistic
Training time reduction percentage
Accuracy comparison across GAN models
Detection Rate (e.g., 95.11% for RF in botnet detection)
Binary vs. Multi-class classification performance metrics
Comparison of statistical features between synthetic and real-world datasets
ROC Curve Analysis
F1 Score
AUC Score
False-Positive Rate
Detection Accuracy
Recall
Specificity
Correlation Coefficient (R)
Root Mean Square Error (RMSE)
Bias
False Alarm Rate (FAR)
Area Under the Precision-Recall Curve (AUC-PR)
Normalized Accuracy
Stability
Accuracy
Precision
Recall
F1 Score
AUC Score
Detection Rate
False Alarm Rate
Mean Square Error (MSE)
Mean Absolute Error (MAE)
Nearest Neighbor Relative Anomaly Factor (NNRAF)
Boxplot-based outlier detection
Detection Latency (used for assessing the performance of an intrusion detection system)
Crossover-Error Rate (evaluating the effectiveness of intrusion detection)
Floating-Point Operations (FLOPs) Reduction (as an efficiency metric for neural architecture search models)
TP
TN
True Positive Rate (TPR)
False Positive Rate (FPR)
False Negative Rate (FNR)
False Alarm Rate (FAR)
ACCURACY
PRECISION
Recall (R)
F-measure (F)
F1-Score
Sensitivity
Training Time
Testing Time
ROC Curve
AUC-ROC
AUC-PR
Confusion Matrix
Matthews Correlation Coefficient (MCC)
Detection Rate
Detection rate (DR)
??UROC
AUPR
G-Mean
BalancedAccuracy
Detection Latency – Used to assess the effectiveness of a 6G intrusion detection system.
Crossover-Error Rate – Evaluates the reliability and robustness of the proposed intrusion detection system.
Training Time – Considered as a performance metric in ensemble deep learning models.
False Positive Rate (FPR) – Evaluated in the smart healthcare intrusion detection framework.
Accuracy
Precision
Recall
F1-score
Matthews Correlation Coefficient (MCC)
Area Under Curve (AUC)
Mean Absolute Error (MAE)
Running Time
Feature Size
Fitness Values
Wilcoxon Signed-Rank Test
Paired-Samples T-Test
Cross-Entropy Loss
Fitting Performance
Information Stolen Rate
Labeling Budget
Statistical  Measures


Accuracy / Correct classification rate / Classification accuracy / Model predictive power
Precision / Positive predictive value (PPV) / True positive rate (TPR)
Recall / Detection rate / Sensitivity / Fraction of positives correctly identified (FPC) / Hit Rate
Specificity / True negative rate (TNR) / True reject rate / Selectivity
F1-Score / F-Ratio / F-measure
False Positive rate (FPR) / Type I error rate / False alarm rate / Fall-out rate
False Negative rate (FNR)
AUC - ROC Area
Kappa Statistic
Mean absolute error (MAE)
Relative Absolute Error (RAE)
Root mean squared error (RMSE)
Matthews correlation coefficient (MCC)
Confusion Matrix
Jaccard score
Hamming loss
Cohen Kappa score
Positive Predictive Value (PPV)
Negative Predictive Value (NPV)
Log loss
GAN (Generative Adversarial Network) loss
F-test
LOF (Local Outlier Factor) Score
ANOVA (Statistical technique)
Mean estimate

ML Evaluation Metrics / Techniques


Training time
Testing time
Prediction time
Detection time
Feature importance scores
coverage capture rate
coverage rate
capture rate
T-Score
Correlation Coefficient
Least Square Regression Error
Maximal Information Compression Index
FFS (Fast Feature Selection)
Sum of Squares
Degree of Dependency (DoD)
GINI Index
G-Mean
Conditional Average Treatment Effect (CPE)
Balanced Accuracy (BACC)
Kolmogorov-Smirnov (KS-statistic)
APIM Score
Macro F1-score
Weighted F1-score

accuracy_score
average_precision_score
balanced_accuracy_score
classification_report
cohen_kappa_score
confusion_matrix
f1_score
fbeta_score
hamming_loss
hinge_loss
jaccard_score
log_loss
matthews_corrcoef
multilabel_confusion_matrix
precision_recall_fscore_support
precision_score
recall_score
roc_auc_score
roc_auc_score
Some also work in the multilabel case:
top_k_accuracy_score
zero_one_loss
average_precision_score
Matthews Correlation Coefficient (MCC)