From ecf4e9073fab51d9f6fd12d60a5c8835490e7cd2 Mon Sep 17 00:00:00 2001 From: Arthur Date: Sun, 15 Feb 2026 22:47:33 +0100 Subject: [PATCH] Remove criterion param from boosting --- sksurv/ensemble/boosting.py | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/sksurv/ensemble/boosting.py b/sksurv/ensemble/boosting.py index af9e548d..a03747c3 100644 --- a/sksurv/ensemble/boosting.py +++ b/sksurv/ensemble/boosting.py @@ -664,13 +664,6 @@ class GradientBoostingSurvivalAnalysis(BaseGradientBoosting, SurvivalAnalysisMix and an increase in bias. Values must be in the range `(0.0, 1.0]`. - criterion : {'friedman_mse', 'squared_error'}, optional, default: 'friedman_mse' - The function to measure the quality of a split. Supported criteria are - 'friedman_mse' for the mean squared error with improvement score by - Friedman, 'squared_error' for mean squared error. The default value of - 'friedman_mse' is generally the best as it can provide a better - approximation in some cases. - min_samples_split : int or float, optional, default: 2 The minimum number of samples required to split an internal node: @@ -878,7 +871,6 @@ def __init__( learning_rate=0.1, n_estimators=100, subsample=1.0, - criterion="friedman_mse", min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, @@ -899,7 +891,6 @@ def __init__( loss=loss, learning_rate=learning_rate, n_estimators=n_estimators, - criterion=criterion, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, @@ -997,7 +988,7 @@ def _fit_stage( for k in range(self.n_trees_per_iteration_): # induce regression tree on the negative gradient tree = DecisionTreeRegressor( - criterion=self.criterion, + criterion="squared_error", splitter="best", max_depth=self.max_depth, min_samples_split=self.min_samples_split,