Measuring Bias in a Model

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prerequisites
beginner Python, Jupyter Notebook, and pandas • basic scikit-learn • basics of machine learning
skills learned
setting up a Google Colab environment • preprocessing a dataset using one-hot encoding • computing bias performance metrics for logistic regression
Adriano Soares Koshiyama, Umar Mohammed, Emre Kazim, and Catherine Inness
1 week · 5-9 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll investigate and report on whether your company’s mortgage application classifier is making fair decisions between male and female applicants. You’ll train a logistic regression classifier on the HMDA dataset, compute performance metrics with a logistic regression classifier, and then chart “equality of opportunity” bias metrics.
This project is a part of the series Mitigate Machine Learning Bias in Mortgage Lending Data.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

The liveProject is for beginner data scientists and software engineers looking to tackle the basic principles of measuring and mitigating ML bias. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basic Python
  • Basic Jupyter Notebook
  • Basic pandas
  • Basic scikit-learn
  • Basic seaborn
TECHNIQUES
  • Basic machine learning

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