🟪 1-Minute Summary
Polynomial features transform input features into higher-degree terms (x → x, x²) and interactions (x₁, x₂ → x₁, x₂, x₁², x₂², x₁x₂). Allows linear regression to fit curves. Degree 2 = quadratic, degree 3 = cubic. Warning: features grow exponentially (2 features, degree 3 = 9 features). Use regularization to prevent overfitting. Visualize first to choose appropriate degree.
🟦 Core Notes (Must-Know)
What are Polynomial Features?
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How to Create Them
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Choosing the Degree
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Feature Explosion Problem
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When to Use Polynomial Features
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“How do polynomial features help?”
- [Answer: Capture non-linear relationships with linear models]
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“What happens with high-degree polynomials?”
- [Answer: Overfitting, exponential feature growth]
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“How many features from 3 original features with degree 2?”
- [Answer: Calculate using formula - includes interactions]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using very high degrees
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Mistake 2: Not scaling features first
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Mistake 3: Not regularizing
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🟩 Mini Example (Quick Application)
Scenario
[Fit parabola with polynomial regression]
Solution
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Ridge
from sklearn.pipeline import Pipeline
# Example to be filled in
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