It estimates the Critical Success Index (a.k.a. threat score, Jaccard Index) for a nominal/categorical predicted-observed dataset.

`jaccardindex`

estimates the Jaccard similarity coefficient
or Jaccard's Index (equivalent to the Critical Success Index `csi`

).

## Usage

```
csi(
data = NULL,
obs,
pred,
pos_level = 2,
atom = FALSE,
tidy = FALSE,
na.rm = TRUE
)
jaccardindex(
data = NULL,
obs,
pred,
pos_level = 2,
atom = FALSE,
tidy = FALSE,
na.rm = TRUE
)
```

## Arguments

- data
(Optional) argument to call an existing data frame containing the data.

- obs
Vector with observed values (character | factor).

- pred
Vector with predicted values (character | factor).

- pos_level
Integer, for binary cases, indicating the order (1|2) of the level corresponding to the positive. Generally, the positive level is the second (2) since following an alpha-numeric order, the most common pairs are

`(Negative | Positive)`

,`(0 | 1)`

,`(FALSE | TRUE)`

. Default : 2.- atom
Logical operator (TRUE/FALSE) to decide if the estimate is made for each class (atom = TRUE) or at a global level (atom = FALSE); Default : FALSE. When dataset is "binomial" atom does not apply.

- tidy
Logical operator (TRUE/FALSE) to decide the type of return. TRUE returns a data.frame, FALSE returns a list; Default : FALSE.

- na.rm
Logic argument to remove rows with missing values (NA). Default is na.rm = TRUE.

## Value

an object of class `numeric`

within a `list`

(if tidy = FALSE) or within a
`data frame`

(if tidy = TRUE).

## Details

The `csi`

is also known as the threat score or the Jaccard Index.
It is a metric especially useful for binary classification tasks, representing the
proportion of true positive (TP) cases with respect to the sum of predicted positive
(PP = TP + FP) and true negative (TN) cases.

\(csi = \frac{TP}{TP + TN + FP} \)

It is bounded between 0 and 1. The closer to 1 the better the classification performance, while zero represents the worst result.

It has been extensively used in meteorology (NOOA) as a verification measure of categorical forecast performance equal to the total number of correct event forecast (hits = TP) divided by the total number of event forecasts plus the number of misses (hits + false alarms + misses = TP + FP + TN). However, the csi has been criticized for not representing an unbiased measure of forecast skill (Schaefer, 1990).

For the formula and more details, see online-documentation

## References

NOOA.
Forecast Verification Glossary.
*Space Weather Prediction Center, NOOA.* https://www.swpc.noaa.gov/sites/default/files/images/u30/Forecast%20Verification%20Glossary.pdf

Schaefer, J.T. (1990).
The critical success index as an indicator of warning skill.
*Weather and Forecasting 5(4): 570-575.* doi:10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2

## Examples

```
# \donttest{
set.seed(123)
# Two-class
binomial_case <- data.frame(labels = sample(c("True","False"), 100, replace = TRUE),
predictions = sample(c("True","False"), 100, replace = TRUE))
# Get csi estimate for two-class case
csi(data = binomial_case, obs = labels, pred = predictions)
#> $csi
#> [1] 0.3768116
#>
# }
```