Are decision trees probabilistic?

Are decision trees probabilistic?

Another disadvantage of decision trees is that they typically do not produce probabilistic predictions. In many applications (e.g. clinical decision making), it is useful to have a predictor that can quantify predictive uncertainty instead of just producing a point estimate.

Does random forest output probability?

Mostly we use Random Forest for classification and we get the output as the class. In Random Forest package by passing parameter “type = prob” then instead of giving us the predicted class of the data point we get the probability.

Is the class probability of a decision tree deterministic?

The class probability of a single tree is the fraction of samples of the same class in a leaf.” the part about “mean predicted class probabilities” indicates that the decision trees are non-deterministic. Furthermore, the lecture by Nando De Freitas here also talks of class probabilities at around 30 minutes.

How are the class probabilities of a tree computed?

“The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf.”. the part about “mean predicted class probabilities” indicates that the decision trees are non-deterministic.

What is the predicted probability of a class?

For a single fully grown tree, I would guess the predicted probability only could be 0 or 1 for any class, because all terminal nodes are pure (same label). If the single tree is not fully grown and/or more trees are grown, then the predicted probability can be a positive rational number from 0 to 1.

Which is the predicted probability of a tree?

So depending on implementation: predicted probability is either (a) the mean terminal leaf probability across all trees or (b) the fraction of trees voting either class. If out-of-bag (OOB) prediction, then only in trees where sample is OOB.