Quantifying uncertainty using conformal prediction
Quantifying the uncertainty of machine learning predictions is becoming increasingly important as machine learning is used more widely in critical applications such as healthcare, finance, and self-driving cars. In these applications, the consequences of incorrect predictions can be severe, making it essential to understand the uncertainty associated with each prediction.
For example, in healthcare, machine learning models are used to make predictions about patient outcomes, such as the likelihood of a disease or the effectiveness of a treatment. These predictions can have a significant impact on patient care and treatment decisions. However, if the model is unable to produce an estimate of its own confidence, it may not be useful and could potentially be risky to rely upon.
In contrast, if the model can provide a measure of its own uncertainty, clinicians can use this information to make more informed decisions about patient...
