APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Lower number of features
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Large number of features
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No such restriction on number of features
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None of the Mentioned
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Detailed explanation-1: -The curse of dimensionality means that KNN performs best with a low number of features. When the number of features increases, then it requires more data.
Detailed explanation-2: -KNN works well with a small number of input variables (p), but struggles when the number of inputs is very large. Each input variable can be considered a dimension of a p-dimensional input space. For example, if you had two input variables x1 and x2, the input space would be 2-dimensional.
Detailed explanation-3: -Usage of KNN Therefore, you can use the KNN algorithm for applications that require high accuracy but that do not require a human-readable model. The quality of the predictions depends on the distance measure. Therefore, the KNN algorithm is suitable for applications for which sufficient domain knowledge is available.