In the context of the Ensemble tool, the prediction function that uses median values is typically used for what purpose?

In the context of the Ensemble tool, using the median value as a prediction function is primarily aimed at handling outliers. The median is a robust measure of central tendency that is less affected by extreme values compared to the mean. When building predictive models, outliers can skew predictions, leading to inaccurate results. By utilizing the median, the ensemble approach mitigates the influence of these outliers, providing more stable and reliable predictions, especially in datasets where outliers could otherwise distort the outcome.

This characteristic makes the median particularly valuable in ensemble methods, where the goal is to aggregate predictions from multiple models to produce a final prediction. When individual model predictions contain outliers, relying on the median for the final prediction helps maintain overall model performance and accuracy.

While the other options focus on different aspects of model performance or evaluation, they do not directly capture the primary reason for opting for a median in ensemble predictions. Reducing overfitting, improving data accuracy, and enhancing model interpretability involve different methodologies and strategies that do not center specifically on outlier management.