Weighted Metrics and Performance Measures for Machine Learning

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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