Module Biocaml_bin_pred (.ml)

Performance measurement of binary classifiers.

This module provides functions to compute various performance measurements of a binary classifier's prediction. Typically, binary classifiers output both a label and a score indicating a confidence level. A ROC curve represents the variation of sensitivity and specificity of the classifier as a function of a score threshold.

module Biocaml_bin_pred: 
type confusion_matrix = private {
   tp : int;
   tn : int;
   fp : int;
   fn : int;
val confusion_matrix : scores:float array ->
labels:bool array -> threshold:float -> confusion_matrix
confusion_matrix ~scores ~labels ~threshold computes a confusion matrix from the classifier scores and example labels, based on a threshold. It assumes that example i has score scores.(i) and label labels.(i), that scores and labels have the same length and that a higher score means increased probability of a true label.
val sensitivity : confusion_matrix -> float
val recall : confusion_matrix -> float
same as sensitivity
val false_positive_rate : confusion_matrix -> float
val accuracy : confusion_matrix -> float
val specificity : confusion_matrix -> float
val positive_predictive_value : confusion_matrix -> float
val precision : confusion_matrix -> float
same as positive_predictive_value
val negative_predictive_value : confusion_matrix -> float
val false_discovery_rate : confusion_matrix -> float
val f1_score : confusion_matrix -> float
val performance_curve : scores:float array ->
labels:bool array -> (float * confusion_matrix) array
performance_curve ~scores ~labels returns the series of confusion matrices obtained by varying the threshold from infinity to neg_infinity. Each confusion matrix comes with the corresponding threshold.
val roc_curve : scores:float array -> labels:bool array -> (float * float) array * float
roc_curve ~scores ~labels returns the ROC curve of the prediction, and the associated Area Under Curve (AUC)
val rp_curve : scores:float array -> labels:bool array -> (float * float) array