[prev in list] [next in list] [prev in thread] [next in thread] 

List:       wekalist
Subject:    [Wekalist] Is it good to use F-measure to perform tuning (select meta-parameter values) on Classific
From:       Edward Wiskers <edwardwiskers () gmail ! com>
Date:       2018-07-31 6:04:06
Message-ID: CAPcuOJ05e+ma9w-ck+wqYCy_FwsX5T3_61DJaasxJkPLwfXH0Q () mail ! gmail ! com
[Download RAW message or body]

[Attachment #2 (multipart/alternative)]


Hi,

To perform tuning (i.e. to select meta-parameter values) on Classification
Tasks usually use such integral criteria, as
F_k_measure ( (k+1)/( k/recall +1/precision ) ), G_measure( (
precision*recall)^0.5 ), breakeven point (point, where precision = recall),
etc. But is it correct?

Any help would be greatly appreciated.

Edward

[Attachment #5 (text/html)]

<div dir="auto"><div dir="auto">Hi,</div><div dir="auto"><br></div><div dir="auto">To \
perform tuning (i.e. to select meta-parameter values) on Classification Tasks usually \
use such integral criteria, as  </div><div dir="auto">F_k_measure ( (k+1)/( k/recall \
+1/precision ) ), G_measure( ( precision*recall)^0.5 ), breakeven point (point, where \
precision = recall), etc. But is it correct?  </div><div dir="auto"><br></div><div \
dir="auto">Any help would be greatly appreciated.  </div><div \
dir="auto"><br></div><div dir="auto">Edward  </div></div>



_______________________________________________
Wekalist mailing list
Send posts to: Wekalist@list.waikato.ac.nz
List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html


[prev in list] [next in list] [prev in thread] [next in thread]