# Machine Learning Evaluation Metrics

## Documentation for package ‘MLmetrics’ version 1.1.3

- DESCRIPTION file.

## Help Pages

MLmetrics-package | MLmetrics: Machine Learning Evaluation Metrics
Accuracy | Accuracy
Area_Under_Curve | Calculate the Area Under the Curve
AUC | Area Under the Receiver Operating Characteristic Curve (ROC AUC)
ConfusionMatrix | Confusion Matrix
F1_Score | F1 Score
FBeta_Score | F-Beta Score
GainAUC | Area Under the Gain Chart
Gini | Gini Coefficient
KS_Stat | Kolmogorov-Smirnov Statistic
LiftAUC | Area Under the Lift Chart
LogLoss | Log loss / Cross-Entropy Loss
MAE | Mean Absolute Error Loss
MAPE | Mean Absolute Percentage Error Loss
MedianAE | Median Absolute Error Loss
MedianAPE | Median Absolute Percentage Error Loss
MLmetrics | MLmetrics: Machine Learning Evaluation Metrics
MSE | Mean Square Error Loss
MultiLogLoss | Multi Class Log Loss
NormalizedGini | Normalized Gini Coefficient
Poisson_LogLoss | Poisson Log loss
PRAUC | Area Under the Precision-Recall Curve (PR AUC)
Precision | Precision
R2_Score | R-Squared (Coefficient of Determination) Regression Score
RAE | Relative Absolute Error Loss
Recall | Recall
RMSE | Root Mean Square Error Loss
RMSLE | Root Mean Squared Logarithmic Error Loss
RMSPE | Root Mean Square Percentage Error Loss
RRSE | Root Relative Squared Error Loss
Sensitivity | Sensitivity
Specificity | Specificity
ZeroOneLoss | Normalized Zero-One Loss (Classification Error Loss)