Single-index evaluation model

For the model, the most important thing is two things

Precision: the model output is the number of ai that is really ai / the number of ai that is output in the model

Recall rate: the number of output ai in the model / the number of real ai

 

Then I want to judge the extreme value and variance of athletes' performance when I was a child

We sometimes fail to judge how good a model is

 

Then we have to use a single indicator to evaluate the quality of the model

Let the precision be a and the recall be b

We use the harmonic mean of a, b to evaluate the model

As for why, I don't know

 

F1 = 1 / ((1 / a)  + (1 / b) )

　  =  (a * b) / (a + b)

 

There may be other factors that affect the quality of your model

You can design an indicator yourself, such as weighted average