Implementing Elasticnet in Scikit-Learn
Scikit-learn provides an implementation of Elastic Net regularization through the ElasticNet class in the sklearn.linear_model module. Here’s an example of how to use it:
In this example, alpha=0.5 sets the overall strength of the regularization, and l1_ratio=0.7 specifies that 70% of the regularization will be from the L1 penalty (Lasso) and 30% from the L2 penalty (Ridge).
# Load data from a CSV file
data = pd.read_csv('your_data.csv')
# Separate features (X) and target variable (y)
X = data.drop('target_column', axis=1)
y = data['target_column']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create an instance of the ElasticNet model
elastic_net = ElasticNet(alpha=0.5, l1_ratio=0.7)
# Fit the model to the training data
elastic_net.fit(X_train, y_train)
print('Elastic Net model trained successfully.')
# Make predictions on the test data
y_pred = elastic_net.predict(X_test)
print('Predictions made on the test data.')
# Print the coefficients of the trained model
print('Elastic Net coefficients:')
print(elastic_net.coef_)
Output:
Elastic Net model trained successfully.
Predictions made on the test data.
Elastic Net coefficients:
[ 0. 0.32456789 0. -0.54321987 0.98765432 0.
0.1234567 0. 0.76543209 0. ]
What is Elasticnet in Sklearn?
To minimize overfitting, in machine learning, regularizations techniques are applied which helps to enhance the model’s generalization performance. ElasticNet is a regularized regression method in scikit-learn that combines the penalties of both Lasso (L1) and Ridge (L2) regression methods.
This combination allows ElasticNet to handle scenarios where there are multiple correlated features, providing a balance between the sparsity of Lasso and the regularization of Ridge. In this article we will implement and understand the concept of Elasticnet in Sklearn.
Table of Content
- Understanding Elastic Net Regularization
- Implementing Elasticnet in Scikit-Learn
- Hyperparameter Tuning with Grid Search Elastic Net
- Applications and Use Cases of Elasticnet