Description Usage Arguments Details Value Examples

View source: R/boost_splines.R

This wrapper function automatically initializes the model by adding all numerical
features of a dataset within a spline base-learner. Categorical features are
dummy encoded and inserted using linear base-learners without intercept. After
initializing the model `boostSpline`

also fits as many iterations as given
by the user through `iters`

.

1 2 3 4 | ```
boostSplines(data, target, optimizer = OptimizerCoordinateDescent$new(),
loss, learning.rate = 0.05, iterations = 100, trace = -1,
degree = 3, n.knots = 20, penalty = 2, differences = 2,
data.source = InMemoryData, data.target = InMemoryData)
``` |

`data` |
[ |

`target` |
[ |

`optimizer` |
[ |

`loss` |
[ |

`learning.rate` |
[ |

`iterations` |
[ |

`trace` |
[ |

`degree` |
[ |

`n.knots` |
[ |

`penalty` |
[ |

`differences` |
[ |

`data.source` |
[ |

`data.target` |
[ |

The returned object is an object of the `Compboost`

class which then can be
used for further analyses (see `?Compboost`

for details).

Usually a model of class `Compboost`

. This model is an `R6`

object
which can be used for retraining, predicting, plotting, and anything described in
`?Compboost`

.

1 2 3 4 5 6 | ```
mod = boostSplines(data = iris, target = "Sepal.Length", loss = LossQuadratic$new())
mod$getBaselearnerNames()
mod$getEstimatedCoef()
table(mod$getSelectedBaselearner())
mod$predict()
mod$plot("Sepal.Width_spline")
``` |

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