Configure ElasticNet "copy_X" Parameter

The copy_X parameter in scikit-learn’s ElasticNet controls whether the input X is copied or overwritten during fitting.

ElasticNet combines L1 and L2 regularization, making it useful for datasets with correlated features. The copy_X parameter determines whether the input data X is copied or modified in place.

Using the default value of True ensures that the input data is not altered, which is safe but consumes more memory. Setting copy_X=False can save memory but may change the input data, which could be problematic in some cases.

The default value for copy_X is True.

In practice, you can set copy_X=False when you need to conserve memory and are aware that the input data will be modified.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train with copy_X=True and copy_X=False
copy_X_values = [True, False]
results = []

for copy_X in copy_X_values:
    en = ElasticNet(copy_X=copy_X, random_state=42)
    en.fit(X_train, y_train)
    y_pred = en.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    results.append((copy_X, mse))
    print(f"copy_X={copy_X}, Mean Squared Error: {mse:.3f}")

Running the example gives an output like:

copy_X=True, Mean Squared Error: 2090.250
copy_X=False, Mean Squared Error: 2090.250

The key steps in this example are:

Generate a synthetic regression dataset with informative and noise features.
Split the data into train and test sets.
Train ElasticNet models with copy_X=True and copy_X=False.
Evaluate the mean squared error of each model on the test set.

Some tips and heuristics for setting copy_X:

Use copy_X=True to avoid altering the input data.
Use copy_X=False to save memory when input data modification is acceptable.
Be mindful of potential side effects when setting copy_X=False.

Issues to consider:

The impact on memory usage vs. the potential alteration of input data.
Suitability for large datasets where memory conservation is crucial.
Modifying input data in place can lead to unintended consequences if the same data is reused elsewhere.

See Also