🟪 1-Minute Summary
Grid Search CV exhaustively tries all combinations of hyperparameters you specify, using cross-validation to evaluate each combination. Returns the best parameters and best score. Automates hyperparameter tuning. Pros: thorough, easy to use. Cons: computationally expensive (exponential with parameters). Alternative: RandomizedSearchCV for faster search.
🟦 Core Notes (Must-Know)
What is Grid Search?
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How It Works
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Parameters vs Hyperparameters
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Grid Search vs Random Search
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Best Practices
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Explain Grid Search CV”
- [Answer: Try all hyperparameter combinations with cross-validation]
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“Why combine Grid Search with Cross Validation?”
- [Answer: Avoid overfitting to validation set]
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“What’s the downside of Grid Search?”
- [Answer: Computationally expensive, exponential combinations]
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“When would you use Random Search instead?”
- [Answer: Large hyperparameter space, limited computation]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Searching too broad a range
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Mistake 2: Not using CV with Grid Search
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Mistake 3: Including data preprocessing in grid
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🟩 Mini Example (Quick Application)
Scenario
[Tune Random Forest hyperparameters]
Solution
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
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