What is KNearestNeighbors?
K-Nearest Neighbors (KNN) is a simple machine-learning method that classifies or predicts something by looking at the “k” closest examples in the data and copying their label or value.
Let's break it down
- K: the number of nearby points you consider (e.g., 3, 5, 7).
- Nearest: the points that are most similar to the one you’re trying to decide about, measured by distance (like straight-line distance).
- Neighbors: those similar points in the dataset.
- Classification: assigning a category (e.g., cat vs. dog).
- Regression: predicting a number (e.g., house price).
- Copying their label/value: you look at what the neighbors are labeled and use the majority vote (for categories) or the average (for numbers) as your answer.
Why does it matter?
KNN is easy to understand and implement, so it’s a great first step for anyone learning how computers can make decisions from data. It also works well when you have a small, clean dataset and need quick, reasonable predictions without heavy math.
Where is it used?
- Recommending movies or products by finding users with similar tastes.
- Detecting spam emails by comparing new messages to known spam and non-spam examples.
- Handwritten digit recognition in simple OCR systems.
- Predicting house prices based on nearby homes with similar features.
Good things about it
- Simple to code and explain to non-technical people.
- No training phase; the model is just the stored data.
- Works well with any number of input features (as long as you can measure distance).
- Naturally adapts to new data - just add more points.
- Performs competitively on many small-to-medium sized problems.
Not-so-good things
- Becomes slow and memory-heavy with large datasets because it must compare to every point.
- Sensitive to irrelevant or differently scaled features; distance can be misleading.
- Struggles with high-dimensional data (the “curse of dimensionality”).
- Requires you to choose the right “k” and distance metric, which can be tricky.