COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
Question
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Nonparametric and Lazy Learning


Parametric and Lazy Learning


Parametric and Eager Learning


Nonparametric and Eager Learning

Detailed explanation1: KNN is a nonparametric and lazy learning algorithm. Nonparametric means there is no assumption for underlying data distribution. In other words, the model structure determined from the dataset.
Detailed explanation2: The knearest neighbors algorithm, also known as KNN or kNN, is a nonparametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.
Detailed explanation3: 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 explanation4: KNN is a lazy learning, nonparametric algorithm. It uses data with several classes to predict the classification of the new sample point. KNN is nonparametric since it doesn’t make any assumptions on the data being studied, i.e., the model is distributed from the data.
Detailed explanation5: KNN is a nonparametric, slow learning algorithm. It predicts the categorization of a new sample point using data from many classes. KNN is nonparametric since it makes no assumptions about the data it is analyzing, i.e. the model is distributed from the data.