2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)

[@jorgeadansorianohernandez9547]

This is one of the clearest explanation i have ever heard, thank you so much for giving the opportunity to the people to go into de ml world.

[@SebastianRaschka]

Nice, I am happy to hear that this explanation turned out to be useful!

[@alibozorgzad8673]

Thank you so much, that was amazing.

[@SebastianRaschka]

Thanks for the kind words!

[@dragosmanailoiu9544]

Great complement to your book thank you for posting your videos on yt

[@SebastianRaschka]

Nice, glad to hear that those complement each other (vs being redundant)

[@ashwinkumarpillai8923]

Thank you for this video. It was very helpful. Liked and Subscribed :)

[@SebastianRaschka]

Thanks, Ashwinkumar 🙌

[@highest6573]

Your video is helpful but I have a question, Suppose that we are using k-NN with just two training points, which have different (binary) labels. Assuming we are using k = 1 and Euclidean distance, what is the decision boundary? Include a drawing with a brief explanation, please how to draw this? The answer is the first video talking about A and B decision right