> 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/logarithmic-loss/logloss.md).
# logloss.md
| | |
| ------- | --------------: |
| logloss | R Documentation |
### Logarithmic Loss
#### Description
A generic S3 function to compute the *logarithmic loss* score for a\
classification model. This function dispatches to S3 methods in`logloss()` 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 `logloss()` 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 `logloss()` in a "safe" validator that\
checks for NA values and matching length, for example:
{% code overflow="wrap" lineNumbers="true" %}
```r
safe_logloss <- function(x, y, ...) {
stopifnot(
!anyNA(x), !anyNA(y),
length(x) == length(y)
)
logloss(x, y, ...)
}
```
{% endcode %}
Apply the same pattern to any custom metric functions to ensure input\
sanity before calling the underlying `C++` code.
#### Usage
```r
## Generic S3 method
## for Logarithmic Loss
logloss(...)
## Generic S3 method
## for weighted Logarithmic Loss
weighted.logloss(...)
```
#### Arguments
| `...` | Arguments passed on to logloss.integer,logloss.factor, weighted.logloss.integer,weighted.logloss.factor
actual
A vector length n, and k levels. Can be of integer or factor.
response
A n \times k \-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.
normalize
A \-value (default: TRUE). If TRUE, the mean
cross-entropy across all observations is returned; otherwise, the sum of
cross-entropies is returned.
w
A \ vector of sample weights.
|
| ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
#### Value
A \
#### References
MacKay, David JC. Information theory, inference and learning algorithms.\
Cambridge university press, 2003.
Kramer, Oliver, and Oliver Kramer. "Scikit-learn." Machine learning for\
evolution strategies (2016): 45-53.
Virtanen, Pauli, et al. "SciPy 1.0: f'undamental algorithms for\
scientific computing in Python." Nature methods 17.3 (2020): 261-272.
#### Examples
```r
## Classes and
## seed
set.seed(1903)
classes <- c("Kebab", "Falafel")
## Generate actual
## and predicted response
## probabilities
actual_classes <- factor(
x = sample(x = classes, size = 1e3, replace = TRUE),
levels = c("Kebab", "Falafel")
)
response <- runif(n = 1e3)
## Logloss
SLmetrics::logloss(
actual = actual_classes,
response = cbind(
response,
1 - response
)
)
## Generate observed
## frequencies
actual_frequency <- sample(10L:100L, size = 1e3, replace = TRUE)
## Poisson Logloss
SLmetrics::logloss(
actual = actual_frequency,
response = response
)
```
```
```
---
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