Understanding inductive conformal predictors

ICP is a variant of conformal prediction that provides valid predictive regions under the same assumptions as classical conformal prediction and has the added benefit of improved computational efficiency, which is particularly useful when dealing with large datasets.

ICPs present a highly efficient and effective solution within the realm of machine learning. They provide a form of conformal prediction that caters to larger datasets, making it highly suitable for real-world applications that involve extensive data volumes. ICPs divide the dataset into training and calibration sets during the model-building process. The training set is used to develop the model, while the calibration set helps calculate the nonconformity scores. This two-step process optimizes computation and delivers precise prediction regions.

Figure 5.3 – Inductive conformal prediction

A predictive model, such as a neural network...