Sentence examples for vote of trees from inspiring English sources

Random Forest uses a collection of multiple decision trees, where each decision tree is a classifier, every split of the decision tree is a classifier, and the final predictions are made by the majority vote of trees.

The Random Forest (RF) classifier is based on growing a large ensemble of classification trees, and the majority vote of the trees determines the class of an observation [ 21].

RF classifier [ 20] constructs independent decision tree classifiers by bagging and random feature selection, and aggregates the outputs of each decision tree in RF to produce one final prediction by a majority vote of the trees.

Replicates are classified based on the majority vote of all trees in the forest.

Each tree in RF will cast a vote for some input x, then the output of the classifier is determined by majority voting of the trees (algorithm 2 in Additional File 1: Appendix).

If ≥50% of trees vote that the candidate is a bright strut, it is marked accordingly.

We take the proportion of trees voting that a given molecule should be classified as active as an approximate measure of the probability of the molecule being active.

The probability was estimated based on the fraction of trees voting for each specific class membership for each test and validation set molecule.

The Random Forest score, defined as the fraction of trees voting for the positive class, varies from 0.0 to 1.0.

The output of the predictor is the fraction of trees voting for the most popular class (in this case PD or SNP).

The forest will be decided and classified base on the most vote, then average of tree's outcome presents the regression.