Models’ performance when using oversampling to balance the training dataset.

Models’ performance when using oversampling to balance the training dataset.

<p>Models’ performance when using oversampling to balance the training dataset.</p>

Saved in:

Bibliographic Details
Main Author:	Anna Beatriz Silva (20989245) (author)
Other Authors:	Elisson da Silva Rocha (17222272) (author), João Fausto Lorenzato (20989248) (author), Patricia Takako Endo (10373252) (author)
Published:	2025
Subjects:	Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified three hybridsampling configurations risk pregnancies earlier including decision tree hybridsampling approach resulted decision tree model machine learning models proper data balancing premature birth predictions predictive models biased predictions obstetric data data imbalance timely interventions sus ). results show random forest preterm births preterm birth performance compared particularly significant neonatal deaths neonatal care minority class main cause factors 2 common problem 70 %, 64 %, 37 weeks 000 cases
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	<p>Models’ performance when using oversampling to balance the training dataset.</p>

Cannot write session to /tmp/vufind_sessions/sess_tgurdvcbgjjmc559am7ukrbi4p