cvms (version 2.0.0)

multinomial_metrics: Select metrics for multinomial evaluation

Description

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.

Usage

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)

Author

Ludvig Renbo Olsen,

See Also

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

Examples

# \donttest{
# 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)
# }