DOC Ensures that AdaBoostClassifier passes numpydoc validation (scikit-learn#20374)

j3nnn1 · glemaitre · samronsin · commit b11b1fca7187 · 2021-11-30T17:23:11.000+01:00
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -10,7 +10,6 @@
 # List of modules ignored when checking for numpydoc validation.
 DOCSTRING_IGNORE_LIST = [
     "ARDRegression",
-    "AdaBoostClassifier",
     "AdaBoostRegressor",
     "AdditiveChi2Sampler",
     "AffinityPropagation",
diff --git a/sklearn/ensemble/_weight_boosting.py b/sklearn/ensemble/_weight_boosting.py
@@ -328,7 +328,7 @@ class AdaBoostClassifier(ClassifierMixin, BaseWeightBoosting):
         The maximum number of estimators at which boosting is terminated.
         In case of perfect fit, the learning procedure is stopped early.
 
-    learning_rate : float, default=1.
+    learning_rate : float, default=1.0
         Weight applied to each classifier at each boosting iteration. A higher
         learning rate increases the contribution of each classifier. There is
         a trade-off between the `learning_rate` and `n_estimators` parameters.
