The K Nearest Neighbor (KNN) algorithm is a simple and practical method used for classification problems in machine learning. It classifies data points based on the closest training examples in the feature space. Here’s how it works:
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Distance Calculation: For a given test data point, the algorithm calculates the distance to all training data points using metrics like Euclidean distance.
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Finding Neighbors: It identifies the 'K' nearest neighbors (data points) based on the calculated distances.
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Voting Mechanism: The algorithm then assigns a class label to the test data point based on the majority class among its K nearest neighbors.
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Prediction: The predicted class is the one that appears most frequently among the K neighbors.
KNN is widely used due to its simplicity and effectiveness, especially in low-dimensional spaces. It can also be used for regression tasks by averaging the values of the K nearest neighbors.
