Sentence examples for multiple decision trees from inspiring English sources

The method automatically selects sample data from higher spatial resolution maps and generates multiple decision trees.

A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables.

Let's use a random forest (RF) classifier5 that will simultaneously consider all genes and grow multiple decision trees to predict the phenotype without assuming a probabilistic model for the read counts.

RF accomplishes this by averaging multiple decision trees.

Additionally, an effective and fast algorithm that builds multiple decision trees in parallel is devised.

Due to the simultaneous use of multiple decision trees and maximum entropy measure, the three aforementioned issues are considerably alleviated.

Feature selection based on the random forests model, which is constructed by aggregating multiple decision tree classifiers, has been widely used.

It constructs multiple decision tree classifiers and obtains the final predictions by voting.

Multiple decision tree models and probabilistic sensitivity analyses were used to compare the administration of magnesium sulphate with the alternative of no treatment.

RF is a statistical classification method based on an ensemble or multiple decision tree approach that incorporates built-in cross-validation during the training phase.

DF is a consensus modeling technique (Tong et al. 2003b) that combines multiple Decision Tree models (hereafter called trees) in a manner that results in more accurate predictions.