🟪 1-Minute Summary
Ridge Regression adds L2 penalty (sum of squared coefficients) to linear regression. Shrinks all coefficients toward zero but never exactly zero. Good for multicollinearity. Hyperparameter α controls strength (higher α = more regularization). Must scale features first. Reduces variance at cost of slight bias. Use when you want to keep all features but reduce overfitting.
🟦 Core Notes (Must-Know)
How Ridge Works
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Formula
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Hyperparameter α (alpha)
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When to Use Ridge
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Ridge vs Linear Regression
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Explain Ridge Regression”
- [Answer: Linear regression + L2 penalty on coefficients]
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“Why does Ridge help with multicollinearity?”
- [Answer: Shrinks correlated coefficients, stabilizes estimates]
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“Can Ridge set coefficients to zero?”
- [Answer: No, only shrinks toward zero (unlike Lasso)]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Not scaling features
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Mistake 2: Using Ridge for feature selection
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🟩 Mini Example (Quick Application)
Scenario
[Regularize linear regression with multicollinearity]
Solution
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
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