Add min_impurity_decrease parameter to gradient boosting classes

sebp · sebp · commit f6d0c7423a90 · 2017-10-16T18:43:38.000+01:00
Requires scikit-learn >= 0.19 See scikit-learn/scikit-learn#8449 Fixes #11
diff --git a/sksurv/ensemble/boosting.py b/sksurv/ensemble/boosting.py
@@ -467,7 +467,8 @@ def __init__(self, loss="coxph", learning_rate=0.1, n_estimators=100,
                  criterion='friedman_mse',
                  min_samples_split=2,
                  min_samples_leaf=1, min_weight_fraction_leaf=0.,
-                 max_depth=3, min_impurity_split=1e-7, random_state=None,
+                 max_depth=3, min_impurity_split=None,
+                 min_impurity_decrease=0., random_state=None,
                  max_features=None, max_leaf_nodes=None,
                  subsample=1.0, dropout_rate=0.0,
                  verbose=0):
@@ -481,6 +482,7 @@ def __init__(self, loss="coxph", learning_rate=0.1, n_estimators=100,
                          min_weight_fraction_leaf=min_weight_fraction_leaf,
                          max_depth=max_depth,
                          min_impurity_split=min_impurity_split,
+                         min_impurity_decrease=min_impurity_decrease,
                          init=ZeroSurvivalEstimator(),
                          random_state=random_state,
                          max_features=max_features,
