# ExtraSurvivalTrees


An extremely-randomized survival forest (extra survival trees).


Usage

``` python
ExtraSurvivalTrees(
    *,
    n_estimators=100,
    max_depth=None,
    min_samples_split=6,
    min_samples_leaf=3,
    max_features="sqrt",
    bootstrap=False,
    oob_score=False,
    engine="numpy",
    random_state=None,
)
```


Like [RandomSurvivalForest](RandomSurvivalForest.md#greenwood.RandomSurvivalForest), but at each node the split threshold for every candidate covariate is drawn at random (uniformly between the covariate's minimum and maximum at that node) rather than optimized. The extra randomization further decorrelates the trees, often lowering variance and speeding up fitting at the cost of a little bias. Following the extra-trees convention, each tree is grown on the full sample (`bootstrap=False`) by default; set `bootstrap=True` to enable out-of-bag scoring and permutation variable importance.


## Parameters


`n_estimators: int = ``100`  
Number of trees in the forest.

`max_depth: int | None = None`  
Maximum depth of each tree (`None` for unlimited).

`min_samples_split: int = ``6`  
Minimum node size eligible for splitting.

`min_samples_leaf: int = ``3`  
Minimum subjects in each child of a split.

`max_features: Any = ``"sqrt"`  
Covariates considered per split: `"sqrt"` (default), `"log2"`, an int, a float, or `None`.

`bootstrap: bool = ``False`  
Whether to grow each tree on a bootstrap sample. `False` by default (the extra-trees convention); required to be `True` for `oob_score` and [variable_importance](GradientBoostingSurvivalAnalysis.md#greenwood.GradientBoostingSurvivalAnalysis.variable_importance).

`oob_score: bool = ``False`  
Whether to compute an out-of-bag concordance estimate after fitting (requires `bootstrap=True`).

`engine: str = ``"numpy"`  
Compute backend for the split search: `"numpy"` (default, deterministic) or `"numba"` (faster on large datasets, requires the `fast` extra). `"auto"` uses Numba when available.

`random_state: Any = None`  
Seed or `numpy.random.Generator` for split randomization and per-node feature selection.


## Examples

Fit extra survival trees on the bundled `lung` dataset:


``` python
import greenwood as gw

# Load data and build a right-censored response
lung = gw.load_dataset("lung", backend="pandas").dropna(subset=["ph.ecog", "ph.karno"])
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
cols = ["age", "sex", "ph.ecog", "ph.karno", "wt.loss"]

# Fit with bootstrap sampling so an out-of-bag score is available
ext = gw.ExtraSurvivalTrees(
    n_estimators=100, bootstrap=True, oob_score=True, random_state=0
).fit(y, lung[cols])
ext
```


    ExtraSurvivalTrees (100 log-rank trees, max_features='sqrt')
    n = 213, events = 151, features = 5
    out-of-bag concordance = 0.6124