# rmsle.md

|       |                 |
| ----- | --------------: |
| rmsle | R Documentation |

### Root Mean Squared Logarithmic Error

#### Description

A generic S3 function to compute the *root mean squared logarithmic*\
*error* score for a regression model. This function dispatches to S3\
methods in `rmsle()` 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 `rmsle()` 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 `rmsle()` in a "safe" validator that checks\
for NA values and matching length, for example:

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

```r
safe_rmsle <- function(x, y, ...) {
  stopifnot(
    !anyNA(x), !anyNA(y),
    length(x) == length(y)
  )
  rmsle(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 Root Mean Squared Logarithmic Error
rmsle(...)

## Generic S3 method
## for weighted Root Mean Squared Logarithmic Error
weighted.rmsle(...)
```

#### Arguments

| `...` | <p>Arguments passed on to <code>rmsle.numeric</code>,<code>weighted.rmsle.numeric</code></p><p><code>actual,predicted</code></p><p>A pair of \<double> vectors of length <code>n</code>.</p><p><code>w</code></p><p>A \<double> vector of sample weights.</p> |
| ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |

#### 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.

#### 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::rmsle(
   actual    = actual_values, 
   predicted = predicted_values
)
```

```

</div>

```


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