How do I implement Lasso and Ridge regression in Python?

How do I implement Lasso and Ridge regression in Python?

You can use the scikit-learn library in Python. Would you like a code example?

Yes, please provide a code example for both Lasso and Ridge.

Sure! Here's a simple example:

from sklearn.linear_model import Lasso, Ridge
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=1, noise=10)

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

# Lasso Regression
lasso = Lasso(alpha=0.1)
lasso.fit(X_train, y_train)

# Ridge Regression
ridge = Ridge(alpha=0.1)
ridge.fit(X_train, y_train)

This code generates synthetic data and fits both Lasso and Ridge regression models.

What are the differences between Lasso and Ridge regression?

Lasso regression adds an L1 penalty which can shrink some coefficients to zero, effectively performing variable selection. Ridge regression adds an L2 penalty, which shrinks coefficients but does not set them to zero. This means Lasso can lead to simpler models.

When should I use Lasso over Ridge?

Use Lasso when you suspect that many features are irrelevant and you want a simpler model. Ridge is better when you believe all features are relevant and want to keep them in the model.