uncertainty in machine learning

Uncertainty in machine learning refers to the model’s awareness of its own confidence in its predictions. It indicates how sure or unsure the model is about a result, helping to identify when the prediction might be less reliable. This can arise from limited or ambiguous data, complex problems, or inherent randomness. Understanding uncertainty allows for better decision-making, as it highlights cases where additional analysis or human judgment may be needed. In essence, it’s the model’s way of expressing doubt, which is crucial for deploying trustworthy and responsible AI systems.