Weighted Metrics and Performance Measures for Machine Learning


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Documentation for package ‘MetricsWeighted’ version 1.0.0

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accuracy Accuracy
AUC Area under the ROC
classification_error Classification Error
deviance_bernoulli Bernoulli Deviance
deviance_gamma Gamma Deviance
deviance_normal Normal Deviance
deviance_poisson Poisson Deviance
deviance_tweedie Tweedie Deviance
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 F1 Score
gini_coefficient Gini Coefficient
logLoss Log Loss/Binary Cross Entropy
mae Mean Absolute Error
mape Mean Absolute Percentage Error
medae Median Absolute Error
mse Mean-Squared Error
multi_metric Multiple Metrics
murphy_diagram Murphy diagram
performance Performance
precision Precision
prop_within Proportion Within
recall Recall
rmse Root-Mean-Squared Error
r_squared Generalized R-Squared
r_squared_bernoulli Pseudo R-Squared regarding Bernoulli deviance
r_squared_gamma Pseudo R-Squared regarding Gamma deviance
r_squared_poisson Pseudo R-Squared regarding Poisson deviance
weighted_cor Weighted Pearson Correlation
weighted_mean Weighted Mean
weighted_median Weighted Median
weighted_quantile Weighted Quantiles
weighted_var Weighted Variance