How can I visualize the results of Lasso and Ridge regression?

How can I visualize the results of Lasso and Ridge regression?

You can visualize the results using plots like coefficient paths or residual plots. Would you like to see an example?

Yes, please show me how to create a coefficient path plot.

To create a coefficient path plot, you can use libraries like Matplotlib or Seaborn in Python. Would you like a code snippet?

That would be great! What does the code look like?

Here's a simple example:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import Lasso, Ridge

# Sample data
X = np.random.rand(100, 10)
y = np.random.rand(100)

# Lasso and Ridge models
lasso = Lasso(alpha=0.1)
ridge = Ridge(alpha=0.1)

# Fit models
lasso.fit(X, y)
ridge.fit(X, y)

# Plotting
plt.plot(lasso.coef_, label='Lasso Coefficients')
plt.plot(ridge.coef_, label='Ridge Coefficients')
plt.title('Coefficient Paths')
plt.legend()
plt.show()

This will give you a visual comparison of the coefficients!

Thanks! How can I interpret the coefficient paths?

The coefficient paths show how the coefficients change with different regularization strengths. In Lasso, some coefficients may shrink to zero, indicating feature selection, while Ridge coefficients tend to shrink but remain non-zero. This helps in understanding the impact of regularization on your model.