Imagine the following scenario.
Train a classifier that classifies an object into one of these n+m classes:
class 1..n -> triggers an action A depending on the predicted class p class n+1..n+m -> triggers an action X independent on the prediction
Used is the
CalibratedClassifierCV of a
scikit-learn. So, the prediction is based on the maximum
Because action X is independent on the prediction, I would very like to combine the m classes (n+1, n+2, ..., n+m) into a single class (to improve training speed, classification speed, memory consumption etc.)
Would that harm the classification result?