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?
How to use this item?

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
0 comment

Monotonic Constraints, Interaction Constraints and Cost-Sensitive Learning

Total time needed: ~4 hours
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.
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

0 comment