Classification
Machine Learning Methods
Overview
Bagging, short for Bootstrap Aggregation, is an ensemble machine learning technique designed to improve the stability and accuracy of algorithms by combining the predictions of multiple models. It works by generating multiple versions of a training dataset through random sampling with replacement (bootstrapping), training a separate model on each subset, and then aggregating their predictions, typically via majority voting (classification) or averaging (regression). Bagging is particularly effective in reducing variance and mitigating overfitting for high-variance models like decision trees. However, its efficacy depends on the diversity among base models and the underlying data distribution. Bagging may not significantly improve performance if the base learners are already stable or if the data lacks sufficient complexity. A limitation is that bagging increases computational cost due to training multiple models, and interpretation of the ensemble becomes less transparent compared to single-model approaches.
Governance Context
From a governance perspective, bagging introduces several obligations and controls, especially in regulated sectors. The EU AI Act and the OECD AI Principles emphasize transparency and accountability, which can be challenging with ensemble methods due to reduced model interpretability. Organizations may be required to document model training procedures, including how data subsets are generated and aggregated, as stipulated in the NIST AI Risk Management Framework (RMF). Additionally, controls such as regular audits of ensemble performance and bias assessment (e.g., under the UK's AI Auditing Framework) are necessary to ensure fair and reliable deployment. These frameworks often require organizations to justify the use of complex ensembles and implement mechanisms for post-hoc explainability, such as feature importance analysis or surrogate models, to meet transparency obligations. Two concrete governance obligations include: (1) maintaining detailed documentation of data sampling, model training, and prediction aggregation processes; and (2) conducting regular bias and performance audits on the ensemble models to detect and mitigate unfair outcomes.
Ethical & Societal Implications
Bagging can enhance fairness and reliability by reducing overfitting and variance, but the opacity of ensemble models poses challenges for transparency and accountability, especially in high-stakes domains. The complexity of bagging may obscure sources of bias, making it harder to identify and mitigate unfair outcomes. Societal trust may be impacted if stakeholders cannot understand or contest automated decisions. Moreover, increased computational demands may have environmental implications, and improper use can perpetuate or amplify biases present in training data.
Key Takeaways
Bagging reduces variance and overfitting by aggregating multiple models trained on bootstrapped data.; It is most effective with high-variance, low-bias base learners such as decision trees.; Governance frameworks require documentation, transparency, and bias assessment for ensemble methods.; Bagging increases computational costs and can reduce model interpretability.; Ethical deployment requires careful consideration of transparency, accountability, and potential bias amplification.; Regular auditing and documentation are critical to responsible bagging deployment.; Bagging may not improve results for stable base learners or simple datasets.