Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Documentation for computing F-scores #34

Open
Luke-in-the-sky opened this issue Jun 3, 2018 · 0 comments
Open

Documentation for computing F-scores #34

Luke-in-the-sky opened this issue Jun 3, 2018 · 0 comments

Comments

@Luke-in-the-sky
Copy link

Hi there,

I think there might be a mistake in the documentation. The Understanding Scaled F-Score section says

The F-Score of these two values is defined as:

$$ \mathcal{F}_\beta(\mbox{prec}, \mbox{freq}) = (1 + \beta^2) \frac{\mbox{prec} \cdot \mbox{freq}}{\beta^2 \cdot \mbox{prec} + \mbox{freq}}. $$

$\beta \in \mathcal{R}^+$ is a scaling factor where frequency is favored if $\beta < 1$, precision if $\beta > 1$

I believe it should say

$\beta \in \mathcal{R}^+$ is a scaling factor where frequency is favored if $\beta > 1$, precision if $\beta < 1$

For beta >> 1

$$ (1 + \beta^2) \frac{\mbox{prec} \cdot \mbox{freq}}{\beta^2 \cdot \mbox{prec} + \mbox{freq}} \approx (\beta^2) \frac{\mbox{prec} \cdot \mbox{freq}}{\beta^2 \cdot \mbox{prec}} = \mbox{freq} $$

and for beta --> 0

$$ (1 + \beta^2) \frac{\mbox{prec} \cdot \mbox{freq}}{\beta^2 \cdot \mbox{prec} + \mbox{freq}} \approx (1) \frac{\mbox{prec} \cdot \mbox{freq}}{0 + \mbox{freq}} = \mbox{prec} $$

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant