Evaluation Metrics for Machine Learning
Accuracy
Absolute Error
Absolute Percent Error
Average Precision at k
Area under the ROC curve (AUC)
Bias
Classification Error
F1 Score
F-beta Score
Log Loss
Mean Log Loss
Mean Absolute Error
Mean Absolute Percent Error
Mean Average Precision at k
Mean Absolute Scaled Error
Median Absolute Error
Mean Quadratic Weighted Kappa
Mean Squared Error
Mean Squared Log Error
Inherit Documentation for Binary Classification Metrics
Inherit Documentation for Classification Metrics
Inherit Documentation for Regression Metrics
Percent Bias
Precision
Relative Absolute Error
Recall
Root Mean Squared Error
Root Mean Squared Log Error
Root Relative Squared Error
Relative Squared Error
Quadratic Weighted Kappa
Squared Error
Squared Log Error
Symmetric Mean Absolute Percentage Error
Sum of Squared Errors
An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.