model weaving

Model weaving is a technique in machine learning where multiple models are combined to improve overall accuracy and robustness. Instead of relying on a single model's predictions, the outputs from several models are integrated—often by averaging or voting—to produce a final decision. This approach leverages the strengths of each individual model and mitigates their weaknesses, leading to more reliable and generalizable results. Think of it as consulting a panel of experts rather than relying on one, ensuring that the final judgment benefits from diverse perspectives and reduces the chance of errors.