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: sigtype confusion_matrix = private {
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tp : |
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tn : |
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fp : |
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fn : |
val confusion_matrix : scores:float array ->
labels:bool array -> threshold:float -> confusion_matrixconfusion_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 -> floatval recall : confusion_matrix -> floatsensitivityval false_positive_rate : confusion_matrix -> floatval accuracy : confusion_matrix -> floatval specificity : confusion_matrix -> floatval positive_predictive_value : confusion_matrix -> floatval precision : confusion_matrix -> floatpositive_predictive_valueval negative_predictive_value : confusion_matrix -> floatval false_discovery_rate : confusion_matrix -> floatval f1_score : confusion_matrix -> floatval performance_curve : scores:float array ->
labels:bool array -> (float * confusion_matrix) arrayperformance_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 * floatroc_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) arrayend