## boosting.Booster


Gradient-boosted trees for a response family, through LightGBM or


Usage

``` python
boosting.Booster(
    family="poisson",
    engine="lightgbm",
    power=None,
    n_rounds=200,
    learning_rate=0.05,
    params=None,
    n_boot=0,
    seed=0,
    alpha=None,
    dispersion_model=False
)
```


XGBoost.


## Parameters


`family: (poisson, gamma, tweedie, gaussian, quantile) = ``"poisson"`  
The engine's objective; a log link for the first three. `"quantile"` fits the [alpha](models.ElasticNet.md#prospicio.models.ElasticNet.alpha) quantile of the response (no link, no offset).

`engine: (lightgbm, xgboost) = ``"lightgbm"`  

`power: float = None`  
Tweedie variance power in (1, 2); required for `"tweedie"`.

`alpha: float = None`  
Quantile level in (0, 1); required for `"quantile"`.

`dispersion_model: bool = ``False`  
Fit a dispersion per row (gamma, Tweedie and Gaussian families).

`n_rounds: int = ``200`  
Boosting rounds.

`learning_rate: float = ``0.05`  

`params: dict = None`  
Further engine parameters (`num_leaves`, `max_depth`, `monotone_constraints`, …), passed as they are.

`n_boot: int = ``0`  
Bootstrap refits for parameter uncertainty in [predict_distribution](models.GlmFit.md#prospicio.models.GlmFit.predict_distribution); 0 gives process noise only.

`seed: int = ``0`  
Seeds the engine and the bootstrap resamples.


## Examples

``` python
>>> from prospicio.boosting import Booster
>>> from prospicio.models import Design
>>> x = [i % 10 / 10 for i in range(200)]
>>> d = Design([x], ["x"], offset=[0.0] * 200)
>>> y = [float(i % 3 == 0) + v for i, v in enumerate(x)]
>>> fit = Booster("poisson", n_rounds=20).fit(d, y)
>>> len(fit.predict(d))
```

200


## Methods

| Name | Description |
|----|----|
| [fit()](#fit) | Fits the booster on [design](models.Coding.md#prospicio.models.Coding.design)'s columns, offset and weights. |

------------------------------------------------------------------------


#### fit()


Fits the booster on [design](models.Coding.md#prospicio.models.Coding.design)'s columns, offset and weights.


Usage

``` python
fit(design, y)
```


##### Parameters


`design: Design`  

`y: list of float`  


##### Returns


`BoosterFit`