COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Most frequent class among the classes of k closest training examples.
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Least frequent class among the classes of k closest training examples.
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Class of the closest point.
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Most frequent class among the classes of the k farthest training examples.
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Detailed explanation-1: -The correct answer to this question is Option A-Most frequent class among the classes of k closest training examples. In k-NN classification, the algorithm predicts a class membership.
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: -KNN makes predictions using the training dataset directly. Predictions are made for a new instance (x) by searching through the entire training set for the K most similar instances (the neighbors) and summarizing the output variable for those K instances.
Detailed explanation-4: -If you keep the value of k as 2, it gives the lowest cross validation accuracy.
Detailed explanation-5: -The value of k in the KNN algorithm is related to the error rate of the model. A small value of k could lead to overfitting as well as a big value of k can lead to underfitting. Overfitting imply that the model is well on the training data but has poor performance when new data is coming.