Evaluation of Binary Classifier with Different Evaluation Metrics

Evaluate binary classifier's performance with respect to user-selected metric (accuracy, auc score, precision, recall, f1 score) for binary phenotype.

Usage

classifierEval(
  obs,
  pred,
  EvalMethod = "accuracy",
  BinarizeThreshold = 0.5,
  print_score = TRUE
)

Arguments

obs: Observed phenotype, vector consists of 0, 1.
pred: Predicted probability of the phenotype, vector consists of any value between 0 and 1
EvalMethod: Binary classifier evaluation method, should be one of the following: 'accuracy' (default), 'auc', 'precision', 'recall', and 'f1'.
BinarizeThreshold: Cutoff threshold to binarize the predicted probability, default is set to 0.5.
print_score: Whether to print out the evaluation score, default is set to TRUE.

Value

An evaluation score corresponding to the selected metric.

Examples

# simulate observed binary phenotype
obs <- rbinom(100,1,0.5)
# simulate predicted probability
pred <- runif(100, 0,1)
# calculate the score
pred_score <- classifierEval(obs, pred, EvalMethod = 'f1', print_score = FALSE)