DOC use notebook-style for plot_train_error_vs_test_error #22440
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glemaitre
reviewed
Feb 11, 2022
| @@ -12,27 +12,30 @@ | |||
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| """ | |||
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| # %% | |||
| # Generate sample data | |||
| # -------------------- | |||
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You can move this section below the author/license
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Hi @glemaitre, I moved the section as you suggested |
| n_samples_train, n_samples_test, n_features = 75, 150, 500 | ||
| np.random.seed(0) | ||
| coef = np.random.randn(n_features) | ||
| coef[50:] = 0.0 # only the top 10 features are impacting the model | ||
| X = np.random.randn(n_samples_train + n_samples_test, n_features) | ||
| y = np.dot(X, coef) | ||
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| # %% |
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I find the code above quite complex. You can replace it with our available helper function from scikit-learn:
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
n_samples_train, n_samples_test, n_features = 75, 150, 500
X, y, coef = make_regression(
n_samples=n_samples_train + n_samples_test,
n_features=n_features,
n_informative=50,
shuffle=False,
noise=1.0,
coef=True,
)
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=n_samples_train, test_size=n_samples_test, shuffle=False
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Hi @glemaitre, I updated the code for generating data. Let me know if I should change anything else! |
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Thanks @brendo-k LGTM. It is always nice to see an improvement in the documentation changing some code from 11 years ago by new helpers that have been proposed on the development path. |
thomasjpfan
pushed a commit
to thomasjpfan/scikit-learn
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this pull request
Mar 1, 2022
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Reference Issues/PRs
Update
examples/linear_model/plot_lasso_and_elasticnet.pyto notebook style, Issue #22406What does this implement/fix? Explain your changes.
Slit example into:
Any other comments?