Sentence examples for bootstrap samples of the from inspiring English sources

Bagging (which stands for Bootstrap Aggregation) consists of an ensemble (or set) of decision trees, where different decision trees in the ensemble are produced by different random samplings (different bootstrap samples) of the original training set [31].

Random Forests (RF) were employed as ensembles of unpruned regression trees created by using bootstrap samples of the training data.

Bagging [6] and boosting [25] are two most popular examples, which work on bootstrap samples of the training set.

The histograms are created from 10,000 bootstrap samples of the test data, as described in Section 4.2.

The trees are randomised firstly by being based on separate bootstrap samples of the data pool, samples of N out of N objects chosen with replacement.

RF is an ensemble of multiple weak un-pruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction [59].

At iteration, the model is created from aweighted bootstrap sample of the training dataset, with.

The Random Forest thus has a number ntree of stochastically different trees, each derived from a fresh bootstrap sample of the training data.

The method was used by constructing 500 unpruned trees using a random sample of sqrt(N) of the available predictors for each tree and a 0.632 bootstrap sample of the data for each tree.

In all cases the experimental setup was identical, and the only difference between the boosting and the bagging algorithms was that bagging used a uniform distribution for each bootstrap sample of the data and uniform weights on the expert models.

The random forest algorithm fits many classification trees to a data set using a subset of predictors and a bootstrap sample of the data, then combines the results (Prasad et al. 2006; Cutler et al. 2007).