______________ models can eliminate prediction bias but are much harder to interpret.

Complex models are designed to capture intricate patterns and relationships within data, which allows them to minimize prediction bias effectively. Unlike simpler models, complex models can utilize a variety of techniques such as ensemble methods, neural networks, or deep learning algorithms. These approaches can handle non-linear relationships and high-dimensional data, leading to improved predictive performance.

However, the trade-off for this increased accuracy is interpretability. Complex models often act as "black boxes," meaning that it can be difficult for practitioners to elucidate how specific predictions are made. This lack of transparency is a significant drawback in scenarios where understanding the reasoning behind predictions is crucial, such as in regulated industries or when conveying results to non-technical stakeholders. Thus, while complex models excel at achieving high accuracy and reducing bias, their interpretative challenges make them less straightforward compared to simpler models, which usually offer better clarity and ease of understanding.