Did you know your data has neighbors too?
The k-nearest neighbors algorithm (KNN), is a fundamental machine learning algorithm used for classification and regression tasks. It operates on the principle of proximity, where it classifies or predicts a target variable based on the majority vote or average of the nearest neighbors in the feature space.
KNN is a non-parametric algorithm, meaning it does not make any assumptions about the underlying data distribution. It relies solely on the available training data and the distance metric to determine the neighbors. The “k” in KNN refers to the number of nearest neighbors considered for classification or regression.
How it works:
Step 1: Select the number K of neighbors
Step 2 Calculate the Euclidean distance of K number of neighbors
Step 3: Tale the K nearest neighbors as per the Euclidean calculated distance
Step 4: Count the number of data points in each category among the k neighbors
Step 5: Assign new data points to the category with the maximum number for the neighbor
When classifying a new data point, KNN identifies the k nearest neighbors based on their feature similarity using distance measures such as Euclidean distance or cosine similarity. The class label or target value of the new data point is determined by taking a majority vote or calculating the average of the labels or values of its k nearest neighbors.
KNN has various applications in different domains, including image recognition, recommender systems, anomaly detection, and bioinformatics. It is a versatile algorithm that is relatively easy to understand and implement.