Sentence examples for many decision trees from inspiring English sources

Random forests is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees.

One example is Random Forests (RF), a combination of many decision trees (see, e.g., [54]).

Classification of data using a Random Forest simply involves traversal of many decision trees, which can be multithreaded easily for fast computation on multi-core processors.

It operates by constructing many decision trees at training time and outputting the class that is the mode of the classes output by individual trees.

From the results we conclude that for this problem domain, complex classifiers, such as the ensemble Random Forest algorithm [44] which induces many decision trees and then combines the results of all trees, and the boosted decision tree [48] generate a more accurate classifier.

In short, RF, as the name suggested, is an ensemble of many decision trees with binary divisions.

Many decision tree algorithms are based on recursion.

Here, a forest refers to a constellation of many decision tree models.

Indeed, his headlock system is just one among many, with decision trees of control and submission organized across the entire body — a comprehensive new paradigm for the ancient sport of grappling.

It combines many binary decision trees built using several bootstrapped learning samples and choosing randomly at each node a subset of explanatory variables [ 27].

By combining an ensemble of many diverse decision trees, RF guards against overfitting and also provides several measures of predictor variable importance.