binomial_metrics: Select metrics for binomial evaluation

In cvms: Cross-Validation for Model Selection

Select metrics for binomial evaluation

Description

\Sexpr[results=rd, stage=render]{lifecycle::badge("experimental")}

Enable/disable metrics for binomial 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

binomial_metrics(
  all = NULL,
  balanced_accuracy = NULL,
  accuracy = NULL,
  f1 = NULL,
  sensitivity = NULL,
  specificity = NULL,
  pos_pred_value = NULL,
  neg_pred_value = NULL,
  auc = NULL,
  lower_ci = NULL,
  upper_ci = NULL,
  kappa = NULL,
  mcc = NULL,
  detection_rate = NULL,
  detection_prevalence = NULL,
  prevalence = NULL,
  false_neg_rate = NULL,
  false_pos_rate = NULL,
  false_discovery_rate = NULL,
  false_omission_rate = NULL,
  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.
balanced_accuracy	Balanced Accuracy (Default: TRUE)
accuracy	Accuracy (Default: FALSE)
f1	F1 (Default: TRUE)
sensitivity	Sensitivity (Default: TRUE)
specificity	Specificity (Default: TRUE)
pos_pred_value	Pos Pred Value (Default: TRUE)
neg_pred_value	Neg Pred Value (Default: TRUE)
auc	AUC (Default: TRUE)
lower_ci	Lower CI (Default: TRUE)
upper_ci	Upper CI (Default: TRUE)
kappa	Kappa (Default: TRUE)
mcc	MCC (Default: TRUE)
detection_rate	Detection Rate (Default: TRUE)
detection_prevalence	Detection Prevalence (Default: TRUE)
prevalence	Prevalence (Default: TRUE)
false_neg_rate	False Neg Rate (Default: FALSE)
false_pos_rate	False Pos Rate (Default: FALSE)
false_discovery_rate	False Discovery Rate (Default: FALSE)
false_omission_rate	False Omission Rate (Default: FALSE)
threat_score	Threat Score (Default: FALSE)
aic	AIC. (Default: FALSE)
aicc	AICc. (Default: FALSE)
bic	BIC. (Default: FALSE)

Author(s)

Ludvig Renbo Olsen, [e-mail]

See Also

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

Examples

# Attach packages
library(cvms)

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

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

# Disable Balanced Accuracy
binomial_metrics(balanced_accuracy = FALSE)

cvms documentation built on July 9, 2023, 6:56 p.m.