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
#>

# }