# hmeasure

The `hmeasure`

package implements a large number of
classification performance metrics, such as the AUC, Error Rate,
sensitivity and specificity, and additionally extends standard libraries
by incorporating recent advances in this area, notably including the
H-measure which was proposed by David Hand as coherent alternative to
the AUC, and further developed by Hand and Anagnostopoulos. For more
info, please visit hmeasure.net.

## Why use it?

This package aspires to become a one-stop-shop for classification
performance metrics, and to support implementations in all popular data
science languages. At the moment it is already covering more metrics
than any other package in R, and is the only one that covers the
H-measure, Specificity at fixed Sensitivity and Minimum Error Rate.

## How do I use it?

The `hmeasure`

packages relies on the scoring interface
that most classifiers support, whereby each example is assigned a score,
so that higher values indicate that the example is more likely to be
labelled positive. For many classifiers, this scores is intended to be a
probability and lies between 0 and 1, but that is not a requirement for
the package. The main function, `HMeasure`

, takes as input
the true labels, and a data frame where each column contains the scores
of a classifier applied on a certain dataset. You may run the following
example where we directly specify the scores to be a noisy version of
the label, with more noise for classifier A than classifier B (this is
known as a the â€˜binormalâ€™ case in the literature):

```
library(hmeasure)
n = 50
set.seed(1)
y = c(rep(1, n), rep(0, n))
scores = data.frame(
A=c(rnorm(n,0,1), rnorm(n,0.5,1)),
B=c(rnorm(n,0,1), rnorm(n,2,1))
)
out = HMeasure(true.class = y, scores = scores)
summary(out)
```

The package also supports a plotting routine that visualizes the ROC
curve, as well as some additional interesting plots that concern
specifically the H-measure:

`plotROC(out)`

## Where can I get it?

The package is available on CRAN and can be downloaded and installed
as per usual:

```
install.packages('hmeasure')
library(hmeasure)
```

For the most recent version, you can install the github version
instead:

`devtools::install_github(canagnos/hmeasure)`