偏りと分散
E  [ ( y − f ^ ( x ) ) 2 ] = ( Bias  [ f ^ ( x ) ] ) 2 + Var  [ f ^ ( x ) ] + σ 2 {\displaystyle \operatorname {E} {\Big [}{\big (}y-{\hat {f}}(x){\big )}^{2}{\Big ]}={\Big (}\operatorname {Bias} {\big [}{\hat {f}}(x){\big ]}{\Big )}^{2}+\operatorname {Var} {\big [}{\hat {f}}(x){\big ]}+\sigma ^{2}}
Bias  [ f ^ ( x ) ] = E  [ f ^ ( x ) ] − f ( x ) {\displaystyle \operatorname {Bias} {\big [}{\hat {f}}(x){\big ]}=\operatorname {E} {\big [}{\hat {f}}(x){\big ]}-f(x)}
Var  [ f ^ ( x ) ] = E  [ f ^ ( x ) 2 ] − E  [ f ^ ( x ) ] 2 . {\displaystyle \operatorname {Var} {\big [}{\hat {f}}(x){\big ]}=\operatorname {E} [{\hat {f}}(x)^{2}]-\operatorname {E} [{\hat {f}}(x)]^{2}.}