COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Non-parametric and Lazy Learning
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Parametric and Lazy Learning
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Parametric and Eager Learning
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Non-parametric and Eager Learning
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Detailed explanation-1: -KNN is a non-parametric and lazy learning algorithm. Non-parametric means there is no assumption for underlying data distribution. In other words, the model structure determined from the dataset.
Detailed explanation-2: -The k-nearest neighbors algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.
Detailed explanation-3: -KNN is a typical example of a lazy learner. It is called lazy not because of its apparent simplicity, but because it doesn’t learn a discriminative function from the training data but memorizes the training dataset instead.
Detailed explanation-4: -KNN is a lazy learning, non-parametric algorithm. It uses data with several classes to predict the classification of the new sample point. KNN is non-parametric since it doesn’t make any assumptions on the data being studied, i.e., the model is distributed from the data.
Detailed explanation-5: -KNN is a non-parametric, slow learning algorithm. It predicts the categorization of a new sample point using data from many classes. KNN is non-parametric since it makes no assumptions about the data it is analyzing, i.e. the model is distributed from the data.