K-nearest neighbors is a non-parametric machine learning model in which the model memorizes the training observation for classifying the unseen test data. It can also be called instance-based learning. This model is often termed as lazy learning, as it does not learn anything during the training phase like regression, random forest, and so on. Instead it starts working only during the testing/evaluation phase to compare the given test observations with nearest training observations, which will take significant time in comparing each test data point. Hence, this technique is not efficient on big data; also, performance does deteriorate when the number of variables is high due to the curse of dimensionality.
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Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
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Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.
Read more about Pratap Dangeti