I have two classes (say 1 and 0), and want to build a classifier. It is possible to use artificial neural networks (ANN) or any “real” classifying method such as SVM or Random Forest. In case of ANN, one can easily estimates confidence level of classification. For example, if we have binary task (with outputs as 0 or 1), and ANN results for some sample is 0.92, one can suppose that ANN “sure” in classification to 1 class. Alternatively, if ANN outputs 0.52, it is considered as unsteady classification to 1 flass.
But if we use Random Forest or SVM how it is possible to confidence level of classification?
For random forests you can look at the vote counts instead of just the winning class. Ie did 92% or 52% of the trees in the ensemble vote for class 1. How you do this will depend on the implementation.