Naive Bayes vs KNN
🎯 Purpose
Use this card to choose between Naive Bayes (NB) and K-Nearest Neighbors (KNN) for fast, interpretable classification — especially in early exploration or small-to-medium-sized projects.
📦 1. Model Comparison
| Model |
Key Feature |
| Naive Bayes |
Probabilistic model using Bayes’ theorem with strong independence assumption |
| KNN |
Instance-based model using distance metrics and majority vote |
🔍 2. When to Use Each
| Scenario |
Best Model |
| Features are categorical or count-based (e.g., word counts) |
✅ Naive Bayes |
| Numeric, dense data where proximity makes sense |
✅ KNN |
| Very small dataset (training speed needed) |
✅ Naive Bayes |
| You want local, example-driven decisions |
✅ KNN |
| You want fast inference at scale |
✅ Naive Bayes |
| You need class probability estimates |
✅ Both (KNN with predict_proba()) |
| Factor |
Naive Bayes |
KNN |
| Training Time |
✅ Extremely fast |
✅ No training (lazy learner) |
| Prediction Time |
✅ Very fast |
🔴 Can be slow (distance to all points) |
| Interpretability |
✅ Moderate (probabilities) |
🟡 Local logic but less intuitive globally |
| Scaling Required |
❌ Not critical |
✅ Mandatory for numeric features |
✅ Decision Checklist
- [ ] Data type reviewed (categorical → NB, numeric → KNN)
- [ ] Feature independence considered (NB assumption)
- [ ] Scaling applied before KNN
- [ ] Performance vs interpretability needs clarified
- [ ] Class probabilities used responsibly in either model
💡 Tip
“Use Naive Bayes when your data is text or tabular — use KNN when your data is shaped by space.”