How can I use Scikit-learn for robust regression?

How can I use Scikit-learn for robust regression?

You can use Scikit-learn's RANSACRegressor or HuberRegressor for robust regression. Would you like a code example?

Yes, please provide a code example using RANSAC.

Sure! Here's a simple example:

from sklearn.linear_model import RANSACRegressor, LinearRegression
import numpy as np

# Generate some data
X = np.random.rand(100, 1) * 10
y = 2 * X.squeeze() + 1 + np.random.randn(100) * 2

# Add some outliers
X[::10] = 0
y[::10] = 0

# Create a RANSAC regressor
model = RANSACRegressor(LinearRegression())
model.fit(X, y)

# Predict using the model
predictions = model.predict(X)

This will help you fit a model that is robust to outliers.

What are the advantages of using RANSAC over traditional regression?

RANSAC is particularly effective in scenarios with a significant number of outliers. It iteratively selects random subsets of the data to fit the model, which helps in identifying the best fit while ignoring outliers. This makes it more robust compared to traditional regression methods.

Are there any other robust regression methods in Scikit-learn?

Yes, besides RANSAC, you can also use HuberRegressor and TheilSenRegressor. Both are designed to handle outliers effectively.