👉 Committee engineering refers to the strategic manipulation of committee parameters and membership in ensemble methods to optimize model performance. By carefully selecting and configuring the weights assigned to individual models within a committee, engineers can enhance the overall predictive power and robustness of the ensemble. This process may involve adjusting weights to balance the contributions of each model, incorporating diverse types of models (e.g., decision trees, neural networks), or even dynamically reconfiguring the committee during training to adapt to data characteristics. The goal is to leverage the collective strengths of multiple models, mitigating individual weaknesses and improving generalization, ultimately leading to more accurate and reliable predictions.
