🟪 1-Minute Summary
Not all relationships are linear. Non-linear modeling captures curves and interactions. Options: (1) Polynomial features (x, x², x³), (2) Interaction terms (x₁*x₂), (3) Non-linear algorithms (trees, neural nets), (4) Transformations (log, sqrt). Still use linear regression with polynomial features - it’s linear in coefficients, not features.
🟦 Core Notes (Must-Know)
When Linear Models Fail
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Approaches to Non-Linearity
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Polynomial Features
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Interaction Terms
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Non-Linear Algorithms
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“How do you handle non-linear relationships?”
- [Answer: Polynomial features, transformations, non-linear models]
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“Is polynomial regression still ’linear’?”
- [Answer: Yes - linear in parameters/coefficients, not features]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using high-degree polynomials
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Mistake 2: Not regularizing polynomial features
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🟩 Mini Example (Quick Application)
Scenario
[Curved relationship example]
Solution
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
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