COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Take the square root of the total data point available in the dataset.
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Take the mean of the total data point available in the dataset.
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Take the variance of the total data point available in the dataset.
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Take the standard deviation of the total data point available in the dataset.
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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: -The choice of k will largely depend on the input data as data with more outliers or noise will likely perform better with higher values of k. Overall, it is recommended to have an odd number for k to avoid ties in classification, and cross-validation tactics can help you choose the optimal k for your dataset.
Detailed explanation-3: -So the value of k indicates the number of training samples that are needed to classify the test sample. Coming to your question, the value of k is non-parametric and a general rule of thumb in choosing the value of k is k = sqrt(N)/2, where N stands for the number of samples in your training dataset.
Detailed explanation-4: -Note that if k is chosen as the total number of observations in the Training Set, all the observations in the Training Set become nearest neighbors. The default value for this option is 1.