DOC increase speed in plot_learning_curve.py (#21628)

Sven Eschlbeck · ogrisel · web-flow · commit 31a75c004d91 · 2021-11-16T15:07:10.000+01:00
* Adapted the number of splits

* Update plot_learning_curve.py

* Update plot_learning_curve.py

Added ``random_state=0`` to call of ``learning_curve``

* Update plot_learning_curve.py

Added ``shuffle=True`` for ``random_state`` to make an impact

* Update examples/model_selection/plot_learning_curve.py

Co-authored-by: Olivier Grisel &lt;olivier.grisel@ensta.org&gt;

Co-authored-by: Olivier Grisel &lt;olivier.grisel@ensta.org&gt;
diff --git a/examples/model_selection/plot_learning_curve.py b/examples/model_selection/plot_learning_curve.py
@@ -179,9 +179,9 @@ def plot_learning_curve(
 X, y = load_digits(return_X_y=True)
 
 title = "Learning Curves (Naive Bayes)"
-# Cross validation with 100 iterations to get smoother mean test and train
+# Cross validation with 50 iterations to get smoother mean test and train
 # score curves, each time with 20% data randomly selected as a validation set.
-cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
+cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
 
 estimator = GaussianNB()
 plot_learning_curve(
@@ -190,7 +190,7 @@ def plot_learning_curve(
 
 title = r"Learning Curves (SVM, RBF kernel, $\gamma=0.001$)"
 # SVC is more expensive so we do a lower number of CV iterations:
-cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
+cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
 estimator = SVC(gamma=0.001)
 plot_learning_curve(
     estimator, title, X, y, axes=axes[:, 1], ylim=(0.7, 1.01), cv=cv, n_jobs=4
