🟪 1-Minute Summary
KNN classifies a data point by looking at the K nearest neighbors and taking a majority vote (classification) or average (regression). It’s a “lazy learner” - no training phase, just stores data. Pros: simple, no assumptions, works for multi-class. Cons: slow prediction, sensitive to scale and irrelevant features, needs optimal K. Always scale features first!
🟦 Core Notes (Must-Know)
How KNN Works
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Choosing K
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Distance Metrics
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- Euclidean
- Manhattan
- Minkowski
Lazy Learning
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Pros & Cons
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🟨 Interview Triggers (What Interviewers Actually Test)
Common Interview Questions
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“How does KNN make predictions?”
- [Answer: Find K nearest neighbors, majority vote (classification) or average (regression)]
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“Why is feature scaling critical for KNN?”
- [Answer: Distance-based - features with large scales dominate]
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“What happens with small K vs large K?”
- [Answer: Small K = complex boundary/overfitting, Large K = simple boundary/underfitting]
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“What’s the time complexity of KNN prediction?”
- [Answer: O(nd) for each prediction - slow!]*
🟥 Common Mistakes (Traps to Avoid)
Mistake 1: Not scaling features
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Mistake 2: Using K=1
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Mistake 3: Using KNN for high-dimensional data
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🟩 Mini Example (Quick Application)
Scenario
[Iris flower classification]
Solution
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# Example to be filled in
# Include finding optimal K
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