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Each decision tree (DT_j) (({j=1,ldots, t})) in the forest is thus constructed using the bootstrap sample (B_j) as follows: for each node of the tree, randomly choose (p') attributes ((p' << p)) and calculate the best split based on one of these (p') attributes; the tree is fully grown and not pruned.
A 'cost-effectiveness acceptability curve' (CEAC) will also be drawn using the bootstrap sample.
Step 2: perform the necessary data analysis using the bootstrap sample as if it were the original sample, and obtain parameter estimates of interest.
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In case that message exchange errors occur, a node will not retransmit the message to a neighbor node but will resample the observation data from the original observation data using the bootstrap sampling (BS) approach [14, 15].
We estimate the variances of the MLEs (PMLEs) using the variances of estimates generated using the bootstrap samples.
This method uses multiple versions of a training set on different models by using the bootstrap (sampling with replacement).
For each bootstrap resample, we used the bootstrap sampled gene's expression measurements as predictor values and applied the KNN to develop the classifier.
The classification power for each gene was evaluated using the bootstrap re-sampling method [29] coupled with two-tailed t-statistics.
The support for individual nodes was assessed using the bootstrap re-sampling method with 1000 replicates [ 79].
Each of the final multivariable models was tested for overfitting by using the bootstrap method (1,000 bootstrap samples of all of the data).
Hazard ratios were compared using the bootstrap method (random re-sampling with replacement; 105 samples) to determine the statistical distribution of (HR1−HR2) from which the relevant P value than was derived.
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CEO of Professional Science Editing for Scientists @ prosciediting.com