👉 Confidence engineering is a set of techniques used to enhance the perceived reliability and accuracy of machine learning models, particularly in scenarios where model predictions are uncertain. It involves methods to quantify and communicate the confidence levels associated with model outputs, helping users understand the certainty of predictions. This is crucial in applications where decisions based on model outputs can have significant consequences, such as healthcare or autonomous driving. Techniques include adjusting prediction thresholds, using ensemble methods to aggregate diverse model outputs, and employing post-processing algorithms that refine predictions based on historical data or domain knowledge. By providing a more nuanced view of prediction uncertainty, confidence engineering aims to improve decision-making processes and build trust in AI systems.
