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How do decision trees predict probability?
In a random forest, multiple decision trees are trained, by using different resamples of your data. In the end, probabilities can be calculated by the proportion of decision trees which vote for each class.
How do you describe a classification tree?
A Classification tree labels, records, and assigns variables to discrete classes. A Classification tree can also provide a measure of confidence that the classification is correct. A Classification tree is built through a process known as binary recursive partitioning.
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.
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 is a random forest a class prediction?
A Random Forest works by aggregating the results of many decision trees. Recently, I was reading about how the RandomForest aggregates the results, and it made me question whether the results from Decision trees are a single class prediction or a probability of each class.
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.