> 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/brier-score/weighted.brier.score.matrix.md).

# weighted.brier.score.matrix.md

|                             |                 |
| --------------------------- | --------------: |
| weighted.brier.score.matrix | R Documentation |

### Brier Score

#### Description

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

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

```r
safe_brier.score <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  brier.score(x, y, ...)
}
```

{% endcode %}

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

#### Usage

```r
## S3 method for class 'matrix'
weighted.brier.score(ok, qk, w, ...)
```

#### Arguments

|       |                                                                                                                                                                                                                                                                                        |
| ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `ok`  | A \<double> indicator matrix with `n` samples and `k` classes.                                                                                                                                                                                                                         |
| `qk`  | A `n \times k` \<double>-matrix of predicted probabilities. The `i`-th row should sum to 1 (i.e., a valid probability distribution over the `k` classes). The first column corresponds to the first factor level in `actual`, the second column to the second factor level, and so on. |
| `w`   | A \<double> vector of sample weights.                                                                                                                                                                                                                                                  |
| `...` | Arguments passed into other methods.                                                                                                                                                                                                                                                   |

#### Value

A \<double>-value

#### References

Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring\
rules, prediction, and estimation." Journal of the American statistical\
Association 102.477 (2007): 359-378.

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
## seed
set.seed(1903)

## The general setup
## with 3 classes
n_obs     <- 10
n_classes <- 3

## Generate indicator matrix
## with observed outcome (ok) and 
## its predicted probability matrix (qk)
ok <- diag(n_classes)[ sample.int(n_classes, n_obs, TRUE), ]
qk <- matrix(runif(n_obs * n_classes), n_obs, n_classes)
qk <- qk / rowSums(qk)


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

## Evaluate performance
SLmetrics::weighted.brier.score(
   ok = ok, 
   qk = qk,
   w  = sample_weights
)
```

```

</div>

```


---

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