What is neighbors?
Neighbors, in the context of the K‑Nearest Neighbors (KNN) algorithm, refer to the data points that are closest to a given point in a dataset. KNN is a simple, instance‑based machine‑learning method that classifies (or predicts) a new item by looking at the “k” most similar items-its nearest neighbors-and letting them vote on the outcome.
Let's break it down
First, you choose a value for k, the number of neighbors to consider. Next, you decide how to measure distance between points (common choices are Euclidean, Manhattan, or cosine distance). When a new data point arrives, the algorithm calculates its distance to every point in the training set. It then selects the k points with the smallest distances - those are the nearest neighbors. For classification, the most common class among those neighbors becomes the predicted class. For regression, the algorithm averages the target values of the neighbors to produce a prediction.
Why does it matter?
KNN is easy to understand and implement, making it a great entry point for learning about machine learning. Because it stores the entire training set, it can adapt to new data without retraining, which is useful for dynamic environments. It works well for problems where the decision boundary is irregular and not easily captured by linear models.
Where is it used?
Recommendation systems (suggesting movies or products based on similar users). Image and text classification tasks where feature similarity is meaningful. Anomaly detection, such as spotting fraudulent transactions by comparing them to normal behavior. Medical diagnosis, where a patient’s symptoms are compared to those of previously diagnosed cases.
Good things about it
Simple to understand and implement - often just a few lines of code. No explicit training phase; the model is the training data itself. Flexible - works for both classification and regression. Can capture complex, non‑linear patterns if enough data is available.
Not-so-good things
Computationally expensive at prediction time because it must compare the new point to every stored example. Memory‑intensive, as it needs to keep the entire training set in memory. Performance heavily depends on the choice of k and the distance metric; poor choices lead to bad results. Sensitive to irrelevant or noisy features, which can distort distance calculations.