Solution
= Solution
The $L$-nearest-neighbour classifier finds the $L$ training predictors nearest to $x$ and returns the majority class among their labels. Increasing $L$ averages more labels and reduces <variance of an estimator>, but uses observations farther from $x$ and therefore increases <bias of an estimator>; decreasing $L$ reverses this <bias-variance tradeoff>.