Sklearn笔记：度量和评分

\[准确率： {\rm{acc}}uracy = \sum\limits_{i = 1}^n {I({y_{true}} = {y_{pred}})} \]

\[Brie{r_{score}} = \frac{1}{N}\sum\limits_{t = 1}^N {({y_{true,t}} - {y_{pred,t}})} \]

\[precision:P = \frac{{TP}}{{TP + FP}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {P_{{\rm{macro}}}} = \frac{1}{n}\sum\limits_{i = 1}^n {{P_i}} \]

\[recall:R = \frac{{TP}}{{TP + FN}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {R_{macro}} = \frac{1}{n}\sum\limits_{i = 1}^n {{R_i}} \]

\[{F_\beta }{\rm{ = }}\frac{{(1 + {\beta ^2})*P*R}}{{({\beta ^2}*P + R)}}{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {F_{\beta {\rm{ - macro}}}} = \frac{{(1 + {\beta ^2})*{P_{macro}}*{R_{macro}}}}{{({\beta ^2}*{P_{macro}} + {R_{macro}})}} \]