This property appears to be overcome by enormously overparameterized models in an as-yet poorly understood phenomenon known as double descent. For 10 points each:
[10m] Name this property that arises because of a decomposition of mean-squared error as the sum of one quantity plus the square of another quantity. Overfitting and underfitting are both risks when ignoring this property.
ANSWER: bias-variance tradeoff [or bias-variance decomposition]
[10h] In a popular nonparametric method, this quantity should scale as “n to the negative one-fifth” so that both terms in the bias-variance decomposition are of the same order. This quantity divides “x minus xj (“x-sub-j”) in the input of a function used by the Nadaraya-Watson estimator.
ANSWER: kernel bandwidth (The method is kernel density estimation.)
[10e] A similar example is tuning the bin width for these diagrams to balance bias and variance. These diagrams are typically plotted as vertical frequency bars.
ANSWER: histograms
<Morrison, Other Science>