README.md

In LqNoob/Machine-Learning-Evaluation-Metrics: Machine Learning Evaluation Metrics

MLmetrics

Linux/Mac: Windows:

Machine Learning Evaluation Metrics

A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.

• Regression: Mean Squared Error Root Mean Squared Error Root Mean Squared Logarithmic Error Root Mean Square Percentage Error Root Relative Squared Error Mean Absolute Error Mean Absolute Percentage Error Median Absolute Error Median Absolute Percentage Error Relative Absolute Error R-Squared (Coefficient of Determination) Regression Score Poisson LogLoss Normalized Gini Coefficient
• Classification: Confusion Matrix Zero-One Loss Accuracy Precision Precision (micro averaged) Precision (macro averaged) Recall Recall (micro averaged) Recall (macro averaged) Sensitivity Specificity F1 Score F1 Score (micro averaged) F1 Score (macro averaged) F-Beta Score Log loss / Cross-Entropy Loss Multi Class Log Loss AUC Gini PRAUC LiftAUC GainAUC Kolmogorov-Smirnov Statistic

To install: * the stable version from CRAN:

install.packages("MLmetrics")

• the latest development version:

devtools::install_github("yanyachen/MLmetrics")

LqNoob/Machine-Learning-Evaluation-Metrics documentation built on May 20, 2019, 3:33 p.m.