👉 Neighbors, short for Nearest Neighbors, is a simple yet powerful machine learning algorithm used for both classification and regression tasks. It operates on the principle of "look around to decide," where the prediction for a given data point is made based on the labels of its nearest neighbors in the feature space. For classification, it assigns a class to a data point by majority vote among its nearest neighbors; for regression, it predicts the target value based on the average or weighted average of the target values of its nearest neighbors. The algorithm requires only the training data to learn the decision boundary, making it non-parametric and highly flexible. However, its performance can be sensitive to the choice of distance metric and the number of neighbors (k), which are crucial hyperparameters that need to be carefully tuned for optimal results.
