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For kNN classifiers, explain the relationship between parameter k and the model’s tendency to overfitting.

For kNN classifiers, explain the relationship between parameter k and the model’s tendency to overfitting.

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`Hey,

Note: Brother in case of any queries, just comment in box I would be very happy to assist all your queries

We should choose K in K - Nearest Neighbour Algorithms wisely.

If we choose our K = 1 , then our algorithm behaves as over fitting and it gives a non - smooth decision surface.

As K increases, our decision surface gets smoother. And,if we choose K = n, then our algorithm behaves as underfitting and it gives a smooth decision surface and everything becomes one class which is the majority class in our DataSet.

So, we should choose K wisely such that it should neither be overfitting nor be underfitting .

Kindly revert for any queries

Thanks.

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