Covers: theory of Cost-Sensitive Learning
Estimated time needed to finish: 60 minutes
Questions this item addresses:
  • What is the motivation for cost sensitive learning?
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This paper discusses the motivation for cost sensitive learning as well as theory and application. The paper explains that cost-sensitive learning is a technique deal with imbalanced dataset and to protect from developing biased classifiers that overfit to majority class.

Author(s) / creator(s) / reference(s)
Charles X. Ling, Victor S. Sheng
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Monotonic Constraints, Interaction Constraints And Cost-sensitive Learning

Contributors
Total time needed: ~4 hours
Objectives
Techniques for placing constraints on machine learning models to ensure they don't end up with illogical or biased results
Potential Use Cases
Bias-mitigation and for injecting domain knowledge into the model
Who is This For ?
INTERMEDIATEMachine Learning Developers focused on bias mitigation and model validation
Click on each of the following annotated items to see details.
Resources6/6
ARTICLE 1. Bias in Machine learning models
  • What is bias and sources of bias in ML models?
10 minutes
PAPER 2. Deontological Ethics By Monotonicity Shape Constraints
  • What is an example of applying monotonic constraints to migitge bias?
60 minutes
ARTICLE 3. Application of Monotonic Constraints in Machine Learning Models
  • How to implement monotonic constraints on a Machine Learning Model?
15 minutes
REPO 4. Feature Interaction Constraints in XGBoost
  • What is a code example of applying feature interaction constraints on an XGBoost model?
60 minutes
PAPER 5. Feature Interactions in XGBoost
  • What is feature interaction in non-linear models?
34 minutes
PAPER 6. Cost-Sensitive Learning and the Class Imbalance Problem
  • What is the motivation for cost sensitive learning?
60 minutes

Concepts Covered

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