🟪 1-Minute Summary
Logistic regression predicts binary outcomes (0/1, yes/no) by estimating probabilities using the sigmoid function. Despite the name, it’s CLASSIFICATION, not regression. Outputs probability (0 to 1), use threshold (default 0.5) to make final decision. Pros: interpretable, outputs probabilities, works well for linearly separable data. Evaluate with accuracy, precision, recall, ROC-AUC.
🟦 Core Notes (Must-Know)
What is Logistic Regression?
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Sigmoid Function
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How It Works
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Binary vs Multi-class
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Decision Boundary
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When to Use
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“Why is it called ‘regression’ if it’s used for classification?”
- [Answer: Historical - it regresses log-odds, outputs probabilities]
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“Explain the sigmoid function and why we use it”
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“How do you handle multi-class classification with logistic regression?”
- [Answer: One-vs-Rest or Multinomial logistic regression]
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“What’s the difference between linear and logistic regression?”
- [Answer: Linear = continuous, Logistic = binary/categorical]
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Using R² to evaluate logistic regression
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Mistake 2: Not calibrating probability threshold
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Mistake 3: Assuming linear decision boundary works for all data
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🟩 Mini Example (Quick Application)
Scenario
[Email spam detection]
Solution
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
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