Choosing the right conformal predictor
Both classical and inductive conformal predictors offer valuable approaches to building reliable machine learning models. However, they each come with unique strengths and weaknesses.
Classical transductive conformal predictors are highly adaptable and do not make any assumptions about data distribution. However, they tend to be computationally expensive, requiring the model’s retraining for each new prediction.
Inductive conformal predictors, conversely, are computationally more efficient, as they only require the model to be trained once.
Choosing the right conformal predictor largely depends on the specific requirements of the problem at hand. Some considerations might include the following:
