Machine Learning Evaluation Metrics
Accuracy
Calculate the Area Under the Curve
Area Under the Receiver Operating Characteristic Curve (ROC AUC)
Confusion Matrix (Data Frame Format)
Confusion Matrix
F1 Score
F-Beta Score
Area Under the Gain Chart
Gini Coefficient
Kolmogorov-Smirnov Statistic
Area Under the Lift Chart
Log loss / Cross-Entropy Loss
Mean Absolute Error Loss
Mean Absolute Percentage Error Loss
Median Absolute Error Loss
Median Absolute Percentage Error Loss
MLmetrics: Machine Learning Evaluation Metrics
Mean Square Error Loss
Multi Class Log Loss
Normalized Gini Coefficient
Poisson Log loss
Area Under the Precision-Recall Curve (PR AUC)
Precision
R-Squared (Coefficient of Determination) Regression Score
Relative Absolute Error Loss
Recall
Root Mean Square Error Loss
Root Mean Squared Logarithmic Error Loss
Root Mean Square Percentage Error Loss
Root Relative Squared Error Loss
Sensitivity
Specificity
Normalized Zero-One Loss (Classification Error Loss)
A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.