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Related Questions
- What are the key differences in how logistic regression and decision trees handle fairness and representation bias?
- Can you explain how logistic regression's reliance on linear relationships affects its ability to capture complex fairness issues?
- How do decision trees' use of recursive partitioning impact their handling of representation bias in high-dimensional data?
- What are some strategies for debiasing logistic regression models, and how effective are they in practice?
- In what ways can decision trees be modified to improve their fairness and representation bias handling, such as through regularization or ensemble methods?
- How do machine learning algorithms like logistic regression and decision trees interact with societal biases, and what are the implications for fairness and representation?
- Can you discuss the trade-offs between model accuracy and fairness in logistic regression and decision trees, and how to balance these competing objectives?
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