Skip to content

MNT: scikit-learn compat: remove criterion param from boosting #582

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
cakedev0 wants to merge 2 commits into
base: main
Choose a base branch
from
+1 −10
Draft

MNT: scikit-learn compat: remove criterion param from boosting #582

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
ecf4e90
Remove criterion param from boosting
cakedev0 Feb 15, 2026
d0e6e58
Merge remote-tracking branch 'upstream/main' into mnt/sklean19/friedm…
cakedev0 May 29, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 1 addition & 10 deletions sksurv/ensemble/boosting.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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:

Expand Down Expand Up @@ -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,
Expand All @@ -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,
Expand Down Expand Up @@ -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,
Expand Down
Loading