package biocaml

  1. Overview
  2. Docs
The OCaml Bioinformatics Library

Install 

dune-project
 Dependency

biocaml.org Readme Edit opam file Versions (5)

Authors

  1. S
    Sebastien Mondet
  2. A
    Ashish Agarwal
  3. P
    Philippe Veber
  4. C
    Christophe Troestler
  5. S
    Sergei Lebedev
  6. D
    David Koppstein
  7. A
    Alexander Gryzlov
  8. F
    Francois Berenger

Maintainers

  1. B
    biocaml@googlegroups.com

Sources

v0.10.0.tar.gz
md5=497e3f2f7128a6ca347d66848da38a3d
sha512=4a76ebbafda3bc944afaff40d69791dfe153a0638ef5d7e6e1bc962b7f991d9545cd0af2d7930b39f8b31dbf067d0603cfa03d9b7a49396ab1ae452df47fd1f3

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

Module Biocaml_unix.Bin_pred

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