1,) How can we use cross validation for getting optimal k in knn algorithm ?
2.)how can we use knn for regression algorithms ?
3.)how does the boundary becomes smother when you increase the value of k , like in classification what do you mean by a smother boundary ?
KNN Quiz doubts
Hello @anshushahi98,
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Split the training data into validation and training set. Keep 80% in training and 20% in testing. Using the 80% data, predict target values for the remaining 20% data with different k values. Now the k for which you have highest validation accuracy is your optimal k.
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For classification, we do voting across the k nearest neighbours and predict the class with the highest vote. In regression, we take the mean of the k nearest neighbour and predict that as the target value of the test instance.
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This link here can help you regarding the smoother boundary question of yours.
Happy Learning 
Thanks
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