# huberloss.numeric.md

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| huberloss.numeric | R Documentation |

### Huber Loss

#### Description

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

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

```r
safe_huberloss <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  huberloss(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 'numeric'
huberloss(actual, predicted, delta = 1, ...)
```

#### Arguments

|                       |                                                                                                                      |
| --------------------- | -------------------------------------------------------------------------------------------------------------------- |
| `actual`, `predicted` | A pair of \<double> vectors of length `n`.                                                                           |
| `delta`               | A \<double>-vector of length `1` (default: `1`). The threshold value for switch between functions (see calculation). |
| `...`                 | 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).

Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for\
scientific computing in Python." Nature methods 17.3 (2020): 261-272.

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

#### Examples

```r
## Generate actual
## and predicted values
actual_values    <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)

## Evaluate performance
SLmetrics::huberloss(
   actual    = actual_values, 
   predicted = predicted_values
)
```

```

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