k=1 means the model just memorizes every training point, so variance goes through the roof and bias drops to basically zero.
Start by defining bias and variance in the context of KNN, then explain how k=1 affects each. Conclude with the trade-off and practical implications.
Pro tip: Mention that k=1 leads to overfitting and high variance, but also note that it has low bias, which is a classic trade-off. Relate it to the bias-variance decomposition to show depth.
Briefly define bias as error from erroneous assumptions and variance as sensitivity to fluctuations in the training set.
With k=1, each prediction is based solely on the nearest neighbor, making the model highly sensitive to individual data points.
Variance is high because small changes in training data can drastically change predictions.
Bias is low because the model can fit complex patterns, but it may also fit noise.
Conclude that k=1 leads to overfitting: low bias but high variance, and discuss when this might be acceptable.
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