Implementing Bias Mitigation Algorithms

Use post-processing methods such as re-weighting, resampling, or adjusting prediction thresholds to reduce bias. Pre-processing methods like data augmentation and in-processing techniques like fairness constraints during training also help ensure equitable retention forecasts across different demographic groups.

Use post-processing methods such as re-weighting, resampling, or adjusting prediction thresholds to reduce bias. Pre-processing methods like data augmentation and in-processing techniques like fairness constraints during training also help ensure equitable retention forecasts across different demographic groups.

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Retention Forecasting Models for Diverse Tech Talent

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