How can I implement linear regression with regularization using NumPy?

How can I implement linear regression with regularization using NumPy?

You can implement linear regression with regularization in NumPy by using Ridge or Lasso regression techniques. Would you like a code example?

Yes, please provide a code example for Ridge regression.

Sure! Here's a simple implementation:

import numpy as np
from sklearn.linear_model import Ridge

# Sample data
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.array([1, 2, 2, 3])

# Create a Ridge regression model
model = Ridge(alpha=1.0)
model.fit(X, y)

# Make predictions
predictions = model.predict(X)
print(predictions)

What does the alpha parameter do in Ridge regression?

The alpha parameter controls the strength of the regularization. A higher alpha value increases the penalty on the coefficients, which can help prevent overfitting.