🟪 1-Minute Summary
Lasso Regression adds L1 penalty (sum of absolute coefficients) to linear regression. Can shrink coefficients to EXACTLY zero, performing automatic feature selection. Hyperparameter α controls strength. Use when you have many features and want to identify the important ones. Sparse solutions make model more interpretable. Must scale features first.
🟦 Core Notes (Must-Know)
How Lasso Works
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Formula
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Feature Selection Property
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When to Use Lasso
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Lasso vs Ridge
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Explain Lasso Regression”
- [Answer: Linear regression + L1 penalty, can zero out coefficients]
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“What’s the main advantage of Lasso over Ridge?”
- [Answer: Automatic feature selection]
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“Why does L1 create sparse solutions but L2 doesn’t?”
- [Answer framework: Geometry of L1 vs L2 constraint]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Not scaling features
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Mistake 2: Using Lasso when you want to keep all features
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🟩 Mini Example (Quick Application)
Scenario
[Feature selection with Lasso]
Solution
from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
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