Covers: theory of Should Ensemble Members Be Calibrated?
0Questions this item addresses:
- How can we evaluate calibrated ensemble members?
How to use this item?
Read sections 3 through 5
Author(s) / creator(s) / reference(s)
Xixin Wu, Mark Gales
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Calibrating Ensemble Members
Contributors
- Objectives
- Learn about developments in ensemble member calibration
- Potential Use Cases
- More accurately assign probabilities to deep learning event outcomes.
- Who is This For ?
- ADVANCEDIf you're looking to ensure the confidence of your model's probability distributions.
Click on each of the following annotated items to see details.
ARTICLE 1. Ensemble Learning
- What is ensemble learning?
10 minutes
PAPER 2. Diverse Ensembles Improve Calibration
- How can we improve calibration of each ensemble member?
10 minutes
PAPER 3. Should Ensemble Members Be Calibrated?
- How can we evaluate calibrated ensemble members?
10 minutes
Concepts Covered
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