package biocaml

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package biocaml
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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.

type confusion_matrix = private {
  1. tp : int;
  2. tn : int;
  3. fp : int;
  4. 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 positive : confusion_matrix -> int
val negative : confusion_matrix -> int
val cardinal : confusion_matrix -> int
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 recall_precision_curve : scores:float array -> labels:bool array -> (float * float) array * float

recall_precision_curve ~scores ~labels returns the RP curve of the prediction, and the associated average precision