University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > Rethinking evaluation for machine learning models

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Rethinking evaluation for machine learning models

Download to your calendar using vCal

If you have a question about this talk, please contact Elre Oldewage .

Machine learning research combines both theoretical and empirical approaches. With the explosion of methods in the recent past and more focus on empirical rather than theoretically driven research, this demands even more care in evaluating these methods to enable a fair comparison. In this discussion session, we aim to highlight and discuss some of these best practices and common pitfalls of evaluating machine learning models. Given that machine learning is a relatively new field as compared to others, this requires a careful dialogue in terms of establishing best practices as compared to a set of prebaked ideas. The aim of this reading session is to enable a more careful and thorough discussion on these topics within CBL .

This talk is part of the Machine Learning Reading Group @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Room SG2, Alison Richard Building, 7 West Road, Cambridge, CB3 9DT Seminar Room 2, Faulkes Gatehouse, Centre for Mathematical Sciences Average on Sundays: 7253.8625 kWh

Other talks

New insights on the turbulent energy cascade from non-equilibrium statistical mechanics Semiconductor-based neutron detector development at QMUL Group Work Spectral theory for Dirac operators with singular potentials Group Work Carrollian momenta from anti de Sitter
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity