APPLICATION OF SUPERVISED LEARNING
MACHINE LEARNINGHARD QUESTIONS
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Following are the two statements given for k-NN algorIthm, which of the statement(s)is/are true?We can choose optimal value of k with the help of cross validationEuclidean distance treats each feature as equally important
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1
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2
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1 and 2
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None of these Both the statements are true
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Explanation:
Detailed explanation-1: -The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value. KNN performs well with multi-label classes, but you must be aware of the outliers.
Detailed explanation-2: -Disadvantages of KNN Algorithm: Always needs to determine the value of K which may be complex some time. The computation cost is high because of calculating the distance between the data points for all the training samples.
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