Module `Biocaml_unix.Bin_pred`Source

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.

Sourcetype confusion_matrix = private {

tp : int;
tn : int;
fp : int;
fn : int;

}

Source

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.

Sourceval positive : confusion_matrix -> int

Sourceval negative : confusion_matrix -> int

Sourceval cardinal : confusion_matrix -> int

Sourceval sensitivity : confusion_matrix -> float

Sourceval recall : confusion_matrix -> float

same as sensitivity

Sourceval false_positive_rate : confusion_matrix -> float

Sourceval accuracy : confusion_matrix -> float

Sourceval specificity : confusion_matrix -> float

Sourceval positive_predictive_value : confusion_matrix -> float

Sourceval precision : confusion_matrix -> float

same as positive_predictive_value

Sourceval negative_predictive_value : confusion_matrix -> float

Sourceval false_discovery_rate : confusion_matrix -> float

Sourceval f1_score : confusion_matrix -> float

Source

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.

Source

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)

Source

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

Install

Dune Dependency

Authors

Maintainers

Sources

doc/biocaml.unix/Biocaml_unix/Bin_pred/index.html

Module `Biocaml_unix.Bin_pred`Source

package biocaml

Install

Dune Dependency

Authors

Maintainers

Sources

doc/biocaml.unix/Biocaml_unix/Bin_pred/index.html

Module Biocaml_unix.Bin_predSource

Module `Biocaml_unix.Bin_pred`Source