# weighted.ckappa.factor.md

|                        |                 |
| ---------------------- | --------------: |
| weighted.ckappa.factor | R Documentation |

### Cohen's `\kappa`-Statistic

#### Description

A generic S3 function to compute the *cohen's `\kappa`-statistic* score\
for a classification model. This function dispatches to S3 methods in`ckappa()` 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 `ckappa()` 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 `ckappa()` in a "safe" validator that checks\
for NA values and matching length, for example:

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

```r
safe_ckappa <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  ckappa(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 cohen's \eqn{\kappa}-statistic
## via S3 dispatching
ckappa(confusion_matrix)

## additional performance metrics
## below
```

{% endcode %}

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

#### Usage

```r
## S3 method for class 'factor'
weighted.ckappa(actual, predicted, w, beta = 0, ...)
```

#### Arguments

|                       |                                                                                                                                                                   |
| --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `actual`, `predicted` | A pair of \<integer> or \<factor> vectors of length `n`, and `k` levels.                                                                                          |
| `w`                   | A \<double> vector of sample weights.                                                                                                                             |
| `beta`                | A \<double> value of length 1 (default: 0). If `\beta \neq 0` the off-diagonals of the confusion matrix are penalized with a factor of `(y_{+} - y_{i,-})^\beta`. |
| `...`                 | Arguments passed into other methods.                                                                                                                              |

#### Value

A \<double>-value

#### 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")
)



## Generate sample
## weights
sample_weights <- runif(
   n = length(actual_classes)
)

## Evaluate performance
SLmetrics::weighted.ckappa(
   actual    = actual_classes, 
   predicted = predicted_classes, 
   w         = sample_weights
)
```

```

</div>

```


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