Understanding Regularization in Ridge Classifier

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When training machine learning models, regularization helps prevent overfitting by penalizing large weights. In Ridge Regression / Ridge Classifier, the regularization strength is controlled by the parameter alpha.

The Question

If we want to apply the RidgeClassifier on X with no regularization, what will be the missing attribute?

Code Snippet:

from sklearn.linear_model import RidgeClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler

estimator = RidgeClassifier(normalize=False, _____=0)
pipe_ridge = make_pipeline(MinMaxScaler(), estimator)
pipe_ridge.fit(X, y)

Options:

  1. ❌ cv

  2. ❌ reg_rate

  3. ✅ alpha

  4. ❌ tol

Explanation

✅ alpha

Why not the others?

  • ❌ cv: Refers to cross-validation folds (not relevant here).

  • ❌ reg_rate: Not an attribute of RidgeClassifier.

  • ❌ tol: Tolerance for stopping criteria, unrelated to regularization.

Example in Practice

from sklearn.linear_model import RidgeClassifier
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)

# RidgeClassifier with no regularization
model = RidgeClassifier(alpha=0)
model.fit(X, y)

print("Coefficients:", model.coef_)

Here, the model will behave like a plain classifier without penalizing large coefficients.

✅ Final Answer

The missing attribute is:

alpha=0

This removes regularization from the Ridge Classifier.

Would you like me to also prepare a side-by-side comparison showing how results change with alpha=0 vs alpha=1?

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