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I have a fairly large dataset with each sample corresponding to one of two target values. I'm using a random forest to assign confidences to new samples for what class they may belong to.

My question is: Does the output of a random forest regressor (mapping each new sample to [0.0, 1.0], where 0 and 1 are the labels for the training samples) differ as a metric for "confidence" from the probabilities given by a random forest classifier on the same data? In other words, would it be valid to train a random forest regressor on the data and interpret the model's outputs on various new samples as the probability that it belongs to class 1?

Any explanation/intuition behind answers would be much appreciated!

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yes, that should be the objective. train on one dataset and test on other datasets. Confidence and accuracy should be same. if not your model might be over fit or under fit.

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