multinomial_metrics function

Select metrics for multinomial evaluation

lifecycle::badge("experimental")

Enable/disable metrics for multinomial evaluation. Can be supplied to the metrics argument in many of the cvms functions.

Note: Some functions may have slightly different defaults than the ones supplied here.

multinomial_metrics(
  all = NULL,
  overall_accuracy = NULL,
  balanced_accuracy = NULL,
  w_balanced_accuracy = NULL,
  accuracy = NULL,
  w_accuracy = NULL,
  f1 = NULL,
  w_f1 = NULL,
  sensitivity = NULL,
  w_sensitivity = NULL,
  specificity = NULL,
  w_specificity = NULL,
  pos_pred_value = NULL,
  w_pos_pred_value = NULL,
  neg_pred_value = NULL,
  w_neg_pred_value = NULL,
  auc = NULL,
  kappa = NULL,
  w_kappa = NULL,
  mcc = NULL,
  detection_rate = NULL,
  w_detection_rate = NULL,
  detection_prevalence = NULL,
  w_detection_prevalence = NULL,
  prevalence = NULL,
  w_prevalence = NULL,
  false_neg_rate = NULL,
  w_false_neg_rate = NULL,
  false_pos_rate = NULL,
  w_false_pos_rate = NULL,
  false_discovery_rate = NULL,
  w_false_discovery_rate = NULL,
  false_omission_rate = NULL,
  w_false_omission_rate = NULL,
  threat_score = NULL,
  w_threat_score = NULL,
  aic = NULL,
  aicc = NULL,
  bic = NULL
)

Arguments

• all: Enable/disable all arguments at once. (Logical)

  Specifying other metrics will overwrite this, why you can use (all = FALSE, accuracy = TRUE) to get only the Accuracy metric.

• overall_accuracy: Overall Accuracy (Default: TRUE)

• balanced_accuracy: Macro Balanced Accuracy (Default: TRUE)

• w_balanced_accuracy: Weighted Balanced Accuracy (Default: FALSE)

• accuracy: Accuracy (Default: FALSE)

• w_accuracy: Weighted Accuracy (Default: FALSE)

• f1: F1 (Default: TRUE)

• w_f1: Weighted F1 (Default: FALSE)

• sensitivity: Sensitivity (Default: TRUE)

• w_sensitivity: Weighted Sensitivity (Default: FALSE)

• specificity: Specificity (Default: TRUE)

• w_specificity: Weighted Specificity (Default: FALSE)

• pos_pred_value: Pos Pred Value (Default: TRUE)

• w_pos_pred_value: Weighted Pos Pred Value (Default: FALSE)

• neg_pred_value: Neg Pred Value (Default: TRUE)

• w_neg_pred_value: Weighted Neg Pred Value (Default: FALSE)

• auc: AUC (Default: FALSE)

• kappa: Kappa (Default: TRUE)

• w_kappa: Weighted Kappa (Default: FALSE)

• mcc: MCC (Default: TRUE)

  Multiclass Matthews Correlation Coefficient.

• detection_rate: Detection Rate (Default: TRUE)

• w_detection_rate: Weighted Detection Rate (Default: FALSE)

• detection_prevalence: Detection Prevalence (Default: TRUE)

• w_detection_prevalence: Weighted Detection Prevalence (Default: FALSE)

• prevalence: Prevalence (Default: TRUE)

• w_prevalence: Weighted Prevalence (Default: FALSE)

• false_neg_rate: False Neg Rate (Default: FALSE)

• w_false_neg_rate: Weighted False Neg Rate (Default: FALSE)

• false_pos_rate: False Pos Rate (Default: FALSE)

• w_false_pos_rate: Weighted False Pos Rate (Default: FALSE)

• false_discovery_rate: False Discovery Rate (Default: FALSE)

• w_false_discovery_rate: Weighted False Discovery Rate (Default: FALSE)

• false_omission_rate: False Omission Rate (Default: FALSE)

• w_false_omission_rate: Weighted False Omission Rate (Default: FALSE)

• threat_score: Threat Score (Default: FALSE)

• w_threat_score: Weighted Threat Score (Default: FALSE)

• aic: AIC. (Default: FALSE)

• aicc: AICc. (Default: FALSE)

• bic: BIC. (Default: FALSE)

Examples

# Attach packages
library(cvms)

# Enable only Balanced Accuracy
multinomial_metrics(all = FALSE, balanced_accuracy = TRUE)

# Enable all but Balanced Accuracy
multinomial_metrics(all = TRUE, balanced_accuracy = FALSE)

# Disable Balanced Accuracy
multinomial_metrics(balanced_accuracy = FALSE)

See Also

Other evaluation functions: binomial_metrics(), confusion_matrix(), evaluate(), evaluate_residuals(), gaussian_metrics()

Author(s)

Ludvig Renbo Olsen, r-pkgs@ludvigolsen.dk