k-Nearest Neighbors (k-NN) is a popular and intuitive algorithm used in machine learning for both classification and regression tasks. It is a non-parametric and lazy learning algorithm, meaning it does not make any assumptions about the underlying data distribution and it only takes action when predictions are requested.
The basic idea behind k-NN is to classify or predict the value of a new datapoint based on the majority vote or average of its k nearest neighbors in the feature space. The choice of k is a hyperparameter that can be optimized based on the dataset and problem at hand.
The choice of distance metric is crucial in k-NN, as it determines the similarity between datapoints. The most commonly used distance metrics are Euclidean distance and Manhattan distance. Euclidean distance calculates the straight-line distance between two points in a 2D or multi-dimensional space. Manhattan distance calculates the distance by summing the absolute differences between the coordinates of two points.
- Computationally expensive: As k-NN needs to compute distances for every datapoint in the dataset, it can be slow and memory-intensive for large datasets.
- Sensitivity to irrelevant features: Since k-NN considers all features equally, irrelevant or noisy features can negatively impact the accuracy of predictions.
- Optimal k-value selection: Choosing the correct value of k is crucial for the accuracy of the k-NN algorithm and requires careful tuning and validation.
k-Nearest Neighbors is a straightforward and effective algorithm for both classification and regression tasks. It makes predictions based on the similarity of new datapoints with their nearest neighbors. Although it has some limitations, k-NN remains a valuable tool in the machine learning toolkit due to its simplicity, versatility, and ability to handle various data types.