R: Weighted Metrics and Performance Measures for Machine Learning

# Weighted Metrics and Performance Measures for Machine Learning

## Documentation for package ‘MetricsWeighted’ version 1.0.3

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

accuracy | Classification Metrics
AUC | Classification Metrics
classification | Classification Metrics
classification_error | Classification Metrics
deviance_bernoulli | Classification Metrics
deviance_gamma | Regression Metrics
deviance_normal | Regression Metrics
deviance_poisson | Regression Metrics
deviance_tweedie | Regression Metrics
elementary_score | Elementary Scoring Function for Expectiles and Quantiles
elementary_score_expectile | Elementary Scoring Function for Expectiles and Quantiles
elementary_score_quantile | Elementary Scoring Function for Expectiles and Quantiles
f1_score | Classification Metrics
gini_coefficient | Classification Metrics
logLoss | Classification Metrics
mae | Regression Metrics
mape | Regression Metrics
medae | Regression Metrics
mse | Regression Metrics
multi_metric | Multiple Metrics
murphy_diagram | Murphy diagram
performance | Performance
precision | Classification Metrics
prop_within | Regression Metrics
recall | Classification Metrics
regression | Regression Metrics
rmse | Regression Metrics
rsquared | Generalized R-Squared
r_squared | Generalized R-Squared
r_squared_bernoulli | Generalized R-Squared
r_squared_gamma | Generalized R-Squared
r_squared_poisson | Generalized R-Squared
weighted_cor | Weighted Pearson Correlation
weighted_mean | Weighted Mean
weighted_median | Weighted Median
weighted_quantile | Weighted Quantiles
weighted_var | Weighted Variance