Gradient descent boosting is an efficient way to optimize any
loss function of a generalized linear or additive model (such as the GAMs
available through the `"mgcv"`

R-package). It furthermore automatically
regularizes the fit, thus the resulting model only contains the covariates
whose baselearners have some influence on the response. Depending on the type
of the `add_biodiversity`

data, either poisson process models or
logistic regressions are estimated. If the `"only_linear"`

term in
train is set to `FALSE`

, splines are added to the estimation, thus
providing a non-linear additive inference.

## Usage

```
engine_gdb(
x,
iter = 2000,
learning_rate = 0.1,
empirical_risk = "inbag",
type = "response",
...
)
```

## Arguments

- x
`distribution()`

(i.e.`BiodiversityDistribution`

) object.- iter
An

`integer`

giving the number of boosting iterations (Default:`2e3L`

).- learning_rate
A bounded

`numeric`

value between`0`

and`1`

defining the shrinkage parameter.- empirical_risk
method for empirical risk calculation. Available options are

`'inbag'`

,`'oobag'`

and`'none'`

. (Default:`'inbag'`

).- type
The mode used for creating posterior predictions. Either making

`"link"`

,`"response"`

or`"class"`

(Default:`"response"`

).- ...
Other variables or control parameters

## Details

: This package requires the `"mboost"`

R-package to be
installed. It is in philosophy somewhat related to the engine_xgboost and
`"XGBoost"`

R-package, however providing some additional desirable
features that make estimation quicker and particularly useful for spatial
projections. Such as for instance the ability to specifically add spatial
baselearners via add_latent_spatial or the specification of monotonically
constrained priors via GDBPrior.

## References

Hofner, B., Mayr, A., Robinzonov, N., & Schmid, M. (2014). Model-based boosting in R: a hands-on tutorial using the R package mboost. Computational statistics, 29(1-2), 3-35.

Hofner, B., Müller, J., Hothorn, T., (2011). Monotonicity-constrained species distribution models. Ecology 92, 1895–901.

Mayr, A., Hofner, B. and Schmid, M. (2012). The importance of knowing when to stop - a sequential stopping rule for component-wise gradient boosting. Methods of Information in Medicine, 51, 178–186.

## See also

Other engine:
`engine_bart()`

,
`engine_breg()`

,
`engine_glm()`

,
`engine_glmnet()`

,
`engine_inla()`

,
`engine_inlabru()`

,
`engine_stan()`

,
`engine_xgboost()`

## Examples

```
if (FALSE) {
# Add GDB as an engine
x <- distribution(background) |> engine_gdb(iter = 1000)
}
```