#### prc

prc implements a few functions to compute

Precision-Recall

curves, and their AUC (area under curve). The implementation is

checked against scikit-learn.

### Installation

Latest release

```
opam install prc
```

Latest commit

```
opam pin add -y prc https://github.com/pveber/prc.git
```

### Motivation

Precision-Recall curves are a useful representation of the performance

of binary classification methods in the case there are many negative

items. Problem is they are difficult to estimate from a finite

sample. As an illustration, compare the following graph obtained in

a particular case where the true precision-recall curve can be

computed analytically (so-called binormal model):

The red curve shows the "true" precision-recall curve while the grey

ones are the empirical estimated obtained from samples of

size 1000. We see that there is a lot of variability, particularly in

the low-recall region.

As a consequence, displaying a precision-recall curve can be

misleading, and it is safer to report an estimate of its area under

curve (AUC) along with a confidence interval. This is what this

library provides.

### Quick test

Demo code is available in the `demo`

directory. To run it, simply

launch an interpreter via a `dune utop demo`

command, and then call

functions in `Prc_demo`

. For instance to get an overview of the

sampling distribution of several estimators under the binormal model,

just type:

```
# Prc_demo.estimator_sampling_distribution ~sample_size:1000 ();;
``` This yields:
![demo](img/estimators_sampling_distribution.png)
where each boxplot represents the distribution of an estimator
(resp. binormal, trapezoidal and average precision) for a binormal
model when drawing finite samples of size 1000.
```

sha256=898322ef7f722e37f332362a24c1ad5f1bf8f8bb396cdb549fb44d59fc4f769a

sha512=c85fc65530195913b20c7b0e1763938f3d45187ca5510a37514f5dec5b4b903a9e40b7621ce8509cf023f1ccb1045fc09b362d716b2e6340cbd5960919a65efc