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Adapted the number of splits in shuffle split to increase speed in plot_learning_curve.py #21628

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Merged
merged 5 commits into from Nov 16, 2021
Merged

Adapted the number of splits in shuffle split to increase speed in plot_learning_curve.py #21628

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82c9f95
Adapted the number of splits
Nov 10, 2021
8300607
Update plot_learning_curve.py
Nov 10, 2021
8c6d656
Update plot_learning_curve.py
Nov 12, 2021
3afe028
Update plot_learning_curve.py
Nov 12, 2021
6a1bc24
Update examples/model_selection/plot_learning_curve.py
Nov 12, 2021
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6 changes: 3 additions & 3 deletions examples/model_selection/plot_learning_curve.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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(
Expand All @@ -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
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