how to set hyperaparmetr alpha for ridge regression sklearn in python

To set the hyperparameter alpha for Ridge Regression in scikit-learn, you can use the Ridge class from the linear_model module. The alpha parameter controls the amount of regularization applied in the model.

Here's an example of how to set the alpha hyperparameter in Ridge Regression:

main.py
from sklearn.linear_model import Ridge

# Create an instance of the Ridge regression model
ridge = Ridge(alpha=0.5)

# Fit the model to your data
ridge.fit(X, y)
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In the code above, the alpha parameter is set to 0.5. You can adjust this value depending on your dataset and the amount of regularization you want to apply. A higher alpha value will increase the amount of regularization, while a lower value will decrease it.

It's important to tune the alpha hyperparameter using techniques like cross-validation to find the optimal value for your specific problem.