Decision Tree vs KNN – A no-BS comparison
For a quick overall comparison between Decision Tree and KNN, we just need to know this:
- KNN works based on the distance between points
- Decision Tree learns “where and under what condition this happened” and right there builds a chain of if-else and branches out
From this simple explanation we can already tell:
- KNN has basically zero training time → it just stores the data
- Decision Tree has training time → it measures, creates conditions, branches → training time is usually higher
After training:
- Decision Tree only has a few conditions → low memory usage
- KNN keeps all the training data → much higher RAM usage
Prediction (inference) time:
- Decision Tree → passes the data through a few conditions → usually fast
- KNN → has to calculate distance to all data points again → slower
Sensitivity to scale:
- KNN works with distances → super sensitive to feature scales (you absolutely have to normalize)
- Decision Tree → doesn’t care at all
Interpretability:
- Decision Tree → you can draw the tree and see exactly why it gave that answer → completely interpretable
- KNN → just says “it was closer to these” → basically no interpretability
Sensitivity to noise:
- KNN looks at local density → noise destroys its accuracy very easily
- Decision Tree can also overfit to noise if you don’t prune or limit depth, but with proper pruning and settings it becomes much more robust
Summary – when is which one better:
- Small to medium dataset + training speed matters more + we don’t need interpretability → KNN is good
- Large dataset + we want fast predictions + we want interpretability → Decision Tree is usually the better choice
By nature KNN is lazy and learns nothing, just measures distances
Decision Tree sits down, thinks, and finds its own paths
This is exactly my view on Decision Tree vs KNN.
If you found this helpful, here are my other ML QuickTips:
https://github.com/PyRz-Tech/PyRz-Tech/blob/main/quick-tips/machine-learning/README.md
Which one do you use more? Drop it in the comments 👇
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