I want to know the exact procedure involved in KNN classification. I understand the bigger picture but I miss the details to implement.
I have 3 pieces of data: Train, Validate and Test.
1) Suppose we have training points $x_1, x_2,\dots,x_N$ each in $\Bbb R^D$ where $D$ is number of features and the labels are $y_1,\dots,y_N$ each in $\Bbb R$ where $N$ is number of training points.
What does training involve? Do I need to pick nearest neighbors for each point $j\in\{1,\dots,N\}$ and relabel the points based on majority vote?
2) What does validation involve?
Given a validation point $x\in\Bbb R^D$ with label $y$ what should I do?
3) Is testing same as validation?
I think the points from $R^D$ that are not $x_1, x_2,\dots,x_N$ should be classified, you should not relabel $x_1, x_2,\dots,x_N$ .
For validation you need points from $R^D$ with valid labels ( the same $x_1, x_2,\dots,x_N$ have).