How can I visualize regularization in Python machine learning models?

How can I visualize regularization in Python machine learning models?

You can use libraries like Matplotlib and Seaborn to visualize the effects of regularization. Would you like a code example?

Yes, please provide a code example for Lasso regularization.

Here's a simple example using Scikit-learn and Matplotlib:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import Lasso
from sklearn.datasets import make_regression

# Generate synthetic data
X, y = make_regression(n_samples=100, n_features=1, noise=10)

# Fit Lasso model with different alpha values
alphas = [0.1, 1.0, 10.0]
plt.scatter(X, y, color='blue')
for alpha in alphas:
    model = Lasso(alpha=alpha)
    model.fit(X, y)
    plt.plot(X, model.predict(X), label=f'Lasso alpha={alpha}')
plt.legend()
plt.title('Lasso Regularization Visualization')
plt.show()

What do the different alpha values represent in Lasso regularization?

In Lasso regularization, the alpha value controls the strength of the penalty. A higher alpha means more regularization, which can lead to simpler models by driving some coefficients to zero.

Can I visualize Ridge regularization in a similar way?

Yes, you can visualize Ridge regularization similarly by using the Ridge class from Scikit-learn. Would you like a code example for that?