> For the complete documentation index, see [llms.txt](https://slmetrics-docs.gitbook.io/v1/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slmetrics-docs.gitbook.io/v1/reference/classification-metrics/jaccard-index/jaccard.md).

# jaccard.md

|         |                 |
| ------- | --------------: |
| jaccard | R Documentation |

### Jaccard Index

#### Description

A generic S3 function to compute the *jaccard index* score for a\
classification model. This function dispatches to S3 methods in`jaccard()` and performs no input validation. If you supply NA values or\
vectors of unequal length (e.g. `length(x) != length(y)`), the\
underlying `C++` code may trigger undefined behavior and crash your `R`\
session.

**Defensive measures**

Because `jaccard()` operates on raw pointers, pointer-level faults (e.g.\
from NA or mismatched length) occur before any `R`-level error handling.\
Wrapping calls in `try()` or `tryCatch()` will *not* prevent `R`-session\
crashes.

To guard against this, wrap `jaccard()` in a "safe" validator that\
checks for NA values and matching length, for example:

{% code overflow="wrap" lineNumbers="true" %}

```r
safe_jaccard <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  jaccard(x, y, ...)
}
```

{% endcode %}

Apply the same pattern to any custom metric functions to ensure input\
sanity before calling the underlying `C++` code.

**Efficient multi-metric evaluation**

For multiple performance evaluations of a classification model, first\
compute the confusion matrix once via `cmatrix()`. All other performance\
metrics can then be derived from this one object via S3 dispatching:

{% code overflow="wrap" lineNumbers="true" %}

```r
## compute confusion matrix
confusion_matrix <- cmatrix(actual, predicted)

## evaluate jaccard index
## via S3 dispatching
jaccard(confusion_matrix)

## additional performance metrics
## below
```

{% endcode %}

The `jaccard.factor()` method calls `cmatrix()` internally, so\
explicitly invoking `jaccard.cmatrix()` yourself avoids duplicate\
computation, yielding significant speed and memory effciency gains when\
you need multiple evaluation metrics.

#### Usage

```r
## Generic S3 method
## for Jaccard Index
jaccard(...)

## Generic S3 method
## for weighted Jaccard Index
weighted.jaccard(...)
```

#### Arguments

| `...` | <p>Arguments passed on to <code>jaccard.factor</code>,<code>weighted.jaccard.factor</code>, <code>jaccard.cmatrix</code></p><p><code>actual,predicted</code></p><p>A pair of \<integer> or \<factor> vectors of length <code>n</code>, and <code>k</code> levels.</p><p><code>estimator</code></p><p>An \<integer>-value of length <code>1</code><br>(default: <code>0</code>).</p><ul><li>0 - a named \<double>-vector of length k<br>(class-wise)</li><li>1 - a \<double> value (Micro averaged metric)</li><li>2 - a \<double> value (Macro averaged metric)</li></ul><p><code>na.rm</code></p><p>A \<logical> value of length <code>1</code><br>(default: TRUE). If TRUE, NA values are removed from the computation.<br>This argument is only relevant when <code>micro != NULL</code>. When<code>na.rm = TRUE</code>, the computation corresponds to<code>sum(c(1, 2, NA), na.rm = TRUE) / length(na.omit(c(1, 2, NA)))</code>.<br>When <code>na.rm = FALSE</code>, the computation corresponds to<code>sum(c(1, 2, NA), na.rm = TRUE) / length(c(1, 2, NA))</code>.</p><p><code>w</code></p><p>A \<double> vector of sample weights.</p><p><code>x</code></p><p>A confusion matrix created <code>cmatrix()</code>.</p> |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

#### Value

If `estimator` is given as

* 0 - a named \<double> vector of length k
* 1 - a \<double> value (Micro averaged metric)
* 2 - a \<double> value (Macro averaged metric)

#### Other names

The specificity has other names depending on research field:

* Critical Success Index, `csi()`
* Threat Score, `tscore()`

#### References

James, Gareth, et al. An introduction to statistical learning. Vol. 112.\
No. 1. New York: springer, 2013.

Hastie, Trevor. "The elements of statistical learning: data mining,\
inference, and prediction." (2009).

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python."\
the Journal of machine Learning research 12 (2011): 2825-2830.

#### Examples

```r
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")

## Generate actual
## and predicted classes
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)

predicted_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)

## Evaluate performance
SLmetrics::jaccard(
   actual    = actual_classes, 
   predicted = predicted_classes
)
```

```

</div>

```


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