Introduction to K-Nearest Neighbors
Explore the fundamentals of the K-Nearest Neighbors algorithm, how it predicts classes based on nearby training samples, and understand the impact of the curse of dimensionality on its performance. This lesson helps you grasp when and how to apply KNN effectively in business machine learning tasks.
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The k-nearest neighbors (KNN) is widely used for classification and is one of the simplest machine learning algorithms. The principle behind the method is to find a predefined number of training samples closest in the distance to the test point and predict the label from these. In simple words, KNN stores the entire dataset as a training algorithm and categorizes the test data point using stored information of its nearest neighbors, where
Example
Let’s consider:
We have a dataset with two classes, A and B, and the entire data, along with its features, f1, and f2, are stored as a training algorithm.
We want to predict the class for red, green, and blue test data points.
Based on the association with their neighboring points, it's straightforward to predict the class for ...
The nearest neighboring data ...