Explain the term 'confidence interval' in machine learning predictions.

The term 'confidence interval' in the context of machine learning predictions refers to a defined range of values derived from a dataset, suggesting where the true value of a parameter (such as a prediction) is expected to fall, with a certain probability. This statistical range provides insight into the uncertainty regarding the predictions made by a model.

When a model makes a prediction, it can be uncertain due to various factors such as noise in the data, quality of the data, and inherent variability. The confidence interval quantifies this uncertainty, giving a better understanding of how reliable the prediction might be. For instance, if the prediction for a certain outcome has a confidence interval of [2, 4], it means that there is a certain level of confidence (often set at 95%) that the true outcome will lie within this range. This is vital for decision-making processes where understanding the reliability of predictions is critical.

This concept is particularly important in domains such as medicine or finance, where making decisions based on predictions carries significant consequences. Using confidence intervals helps practitioners make more informed decisions by acknowledging the inherent uncertainty in their predictions.