How can I implement Elastic Net in Python?

How can I implement Elastic Net in Python?

You can use the ElasticNet class from the sklearn.linear_model module. Would you like a code example?

Yes, please provide a code example.

Here's a simple example:

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

# Generate synthetic data
X, y = make_regression(n_samples=100, n_features=20, noise=0.1)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create Elastic Net model
model = ElasticNet(alpha=1.0, l1_ratio=0.5)

# Fit the model
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

What do the parameters alpha and l1_ratio mean?

alpha controls the strength of the regularization, while l1_ratio determines the mix between Lasso and Ridge regression.

Can I tune these parameters?

Yes, you can use techniques like Grid Search or Random Search to find the best values for alpha and l1_ratio.