Configure ElasticNet "selection" Parameter

The selection parameter in scikit-learn’s ElasticNet controls the strategy used to select the coefficients during the iterative process.

ElasticNet is a linear regression model that combines both L1 and L2 regularization. It is particularly useful when there are multiple correlated features, as it can select groups of correlated variables.

The selection parameter can take two values: 'cyclic' or 'random'. The 'cyclic' option updates the coefficients in a fixed order, while 'random' updates them in a random order, which can lead to faster convergence on some datasets.

The default value for selection is 'cyclic'.

In practice, 'cyclic' is commonly used for smaller datasets, while 'random' can be more efficient for larger datasets.

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 dataset
X, y = make_regression(n_samples=1000, n_features=20, 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 different selection values
selection_values = ['cyclic', 'random']
errors = []

for selection in selection_values:
    enet = ElasticNet(selection=selection, random_state=42)
    enet.fit(X_train, y_train)
    y_pred = enet.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    errors.append(mse)
    print(f"selection={selection}, Mean Squared Error: {mse:.3f}")

Running the example gives an output like:

selection=cyclic, Mean Squared Error: 4638.839
selection=random, Mean Squared Error: 4639.061

The key steps in this example are:

Generate a synthetic regression dataset with informative features.
Split the data into train and test sets.
Train ElasticNet models with different selection values.
Evaluate the mean squared error of each model on the test set.

Some tips and heuristics for setting selection:

Use 'cyclic' for smaller datasets as it is the default and often sufficient.
Try 'random' for larger datasets to potentially achieve faster convergence.
Monitor the convergence speed and accuracy for both options to choose the best one for your specific dataset.

Issues to consider:

The optimal selection strategy may depend on the dataset size and feature correlation.
'random' selection can sometimes lead to faster convergence but may not always improve performance.
It is important to cross-validate and compare results for both strategies to ensure the best model performance.

See Also