APPLICATION OF SUPERVISED LEARNING
SUPERVISED 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 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-2: -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.
Detailed explanation-3: -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.