COMPUTER SCIENCE AND ENGINEERING
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 algorithm is a good choice if you have a small dataset and the data is noise free and labeled. When the data set is small, the classifier completes execution in shorter time duration. If your dataset is large, then KNN, without any hacks, is of no use.
Detailed explanation-3: -The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems.
Detailed explanation-4: -Feature scaling is essential for machine learning algorithms that calculate distances between data. If not scaled, the feature with a higher value range starts dominating when calculating distances. KNN which uses Euclidean distance is one such algorithm which essentially require scaling.