# Using Random Forest Regression vs Classification for Confidence Metrics

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!

But why would you do that? Random forest classifier does predict the probabilities, scikit-learn has the predict_proba function for that purpose.