What is Lasso regularization in Python?

What is Lasso regularization in Python?

Lasso regularization, or L1 regularization, is a technique used in regression models to prevent overfitting by adding a penalty equal to the absolute value of the magnitude of coefficients.

How does Lasso differ from Ridge regularization?

Lasso uses L1 penalty which can shrink some coefficients to zero, effectively performing variable selection, while Ridge uses L2 penalty which shrinks coefficients but does not set them to zero.

Can you show me a simple example of Lasso in Python?

Sure! You can use the Lasso class from sklearn.linear_model. Here's a basic example:

from sklearn.linear_model import Lasso
from sklearn.datasets import make_regression

X, y = make_regression(n_samples=100, n_features=10, noise=0.1)
model = Lasso(alpha=0.1)
model.fit(X, y)
print(model.coef_)