Regularization

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🎯 Purpose

Use this decision card to choose between Ridge, Lasso, and ElasticNet regression based on your dataset structure, modeling goals, and need for feature selection or multicollinearity control.

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🧠 1. Method Comparison

Method	Penalty	Primary Effect
Ridge	L2 (squared sum)	Shrinks coefficients, keeps all
Lasso	L1 (absolute sum)	Shrinks + selects (sparse output)
ElasticNet	L1 + L2 hybrid	Balance between shrink + select

✔️ All methods reduce overfitting and handle multicollinearity.

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📌 2. When to Use Each

Use Case	Choose...
High multicollinearity, keep all features	Ridge
Need automatic feature selection	Lasso
Many correlated predictors + want balance	ElasticNet

✔️ ElasticNet is ideal when Lasso is too aggressive and Ridge retains too much noise.

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⚙️ 3. Tuning Considerations

Param	Notes
alpha	Higher = stronger regularization
l1_ratio (ElasticNet)	0 = Ridge, 1 = Lasso, 0.5 = balance

# ElasticNet example:
ElasticNet(alpha=0.1, l1_ratio=0.5)

✔️ Always scale your features before regularization.

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🔍 4. Output Expectations

Method	What You'll See
Ridge	All features retained, small coefficients
Lasso	Some coefficients driven to zero (selected model)
ElasticNet	Shrunk + selected balance, some near-zero coefficients

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✅ Decision Checklist

• [ ] Multicollinearity detected or suspected
• [ ] Need feature selection? → Lasso or ElasticNet
• [ ] Need only shrinkage? → Ridge
• [ ] All features scaled
• [ ] Tuning (alpha, l1_ratio) cross-validated

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💡 Tip

"Use Ridge to stabilize, Lasso to simplify, and ElasticNet to balance the two."