What are Ridge Regression and its benefits?

What are Ridge Regression and its benefits?

Ridge Regression is a type of linear regression that includes a regularization term. It helps prevent overfitting by adding a penalty for large coefficients.

How does Ridge Regression differ from Lasso Regression?

While both Ridge and Lasso are regularization techniques, Ridge uses L2 regularization (squared coefficients), whereas Lasso uses L1 regularization (absolute values), which can lead to sparse solutions.

Can you show me a simple example of Ridge Regression in Python?

Sure! Here's a basic example using scikit-learn:

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

X, y = make_regression(n_samples=100, n_features=1, noise=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)
print(ridge.score(X_test, y_test))

What are the hyperparameters I should consider when using Ridge Regression?

The main hyperparameter in Ridge Regression is the alpha value, which controls the strength of the regularization. You can also consider the solver used for optimization.