Sentence examples for bootstrapped samples of the from inspiring English sources

Exact(9)

This method involved building a set of regression trees based on bootstrapped samples of the belowground C data.

First, each sampled unit is identically replicated a number of times equal to its sampling weight (rounded to the closest integer), so as to generate a sample that replicates the full target population.23The resulting expanded datasets are fully representative of the target populations and can be used to extract sequences of S bootstrapped samples of the same size of the original country samples.

We fit a Random Forest model to training data sets consisting of bootstrapped samples of the original sample size, with the remaining unused samples used as a validation data set.

Applying factor analysis [10] to 1000 bootstrapped samples of the 20 sleep-wake traits over the 24-hr period allowed for an unbiased identification of structure within the multitude of variables (Table 1 and Supporting Information Table S2 for the bootstrapped 95% confidence intervals).

This algorithm constructs many decision trees on bootstrapped samples of the data set, averaging each estimate to make final predictions.

Standard errors and statistical tests on performance measured as a function of viewing duration following the final change-point on the dots-reversal task were based on bootstrapped samples of the behavioral data or model predictions.

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Similar(51)

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.

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

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

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.

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