binomial_metrics {cvms}R Documentation

Select metrics for binomial evaluation

Description

[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, r-pkgs@ludvigolsen.dk

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)


[Package cvms version 1.3.3 Index]