Library
Module
Module type
Parameter
Class
Class type
Precision-Recall curve construction and AUC computation
References: 1
The binormal assumption on precision-recall curves. Kay H. Brodersen, Cheng Soon Ong, Klaas E. Stephan and Joachim M. Buhmann
2
Area Under the Precision-Recall Curve: Point Estimates and Confidence Intervals. Kendrick Boyd, Kevin H. Eng and C. David Page
3
Precision-Recall-Gain Curves: PR Analysis Done Right. Peter A. Flach and Meelis Kull
4
The Relationship Between Precision-Recall and ROC Curves. Jesse Davis and Mark Goadrich
5
Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. M.J.J. Scott, M. Niranjan, R.W. Prager
val n_pos : dataset -> int
Number of positive items in the dataset
val operating_points : dataset -> (float * float * float) list
operating_points d
computes the list of score threshold, recall and precision triplets, sorted by decreasing threshold.
val auc_trapezoidal_lt : dataset -> float
AUC lower triangular estimator (see 2
for reference)
val auc_average_precision : dataset -> float
AUC average precision (see 2
for reference)
logit_confidence_interval ~alpha ~theta_hat ~n
computes an asymptotically valid confidence interval at level 1 - alpha
, when the estimate theta_hat
was obtained from a sample with n_pos
positive observations.
val bootstrap_confidence_interval :
?niter:int ->
alpha:float ->
Gsl.Rng.t ->
dataset ->
f:(dataset -> float) ->
float * float
bootstrap_confidence_interval ?niter ~alpha rng d ~f
computes a bootstrap confidence interval at level 1 - alpha
for the values produces by f
, using n_iter
bootstrap iterations.
module Binormal_model : sig ... end
Binormal model