Random forest improves the prediction accuracy by first generating bootstrap subsamples of the original data with a reduced number of predictor variables and then growing unpruned binary classification trees for each subsample.
In contrast, a data set X 0 providing for the distribution of the test statistic under the alternative hypothesis is attained by separately bootstrapping j=1…g subsamples of size n (j) using the original subsamples' item parameter estimates (hat {beta }_{i}^{(j)}).
Lastly, unlike the conventional boosting algorithm application, we only exploit bootstrap subsamples generated by the algorithm and dismiss trained SVMs for the following reasons: We are primarily interested in feature ranking and not the aggregation of weak hypotheses.
23 Secondly, to determine whether lack of homogeneity between the different studies in the joint cohort influenced results we used comparative analysis of systematically drawn subsamples of the joint cohort, so called bootstrap analysis.
Finally, we undertook analyses of random subsamples of the data, which showed general consistency among subsamples in results and inferences.
Setting Subsamples of the National Longitudinal Study of Adolescent Health Add Healthh).
These three subsamples were the primary subsamples of the pregnant women.
P value was computed by bootstrapping random subsamples (see the 'Sequence similarity and subsampling analyses' section of the Methods).
Dependence of |D'| and ron the sample size was demonstrated by taking bootstrap subsamples [ 2] (with replacement) of size 22, 55, 111, 222, 444, 665 and 877 (1/16, 1/86, 1/4, 1/2, 1/2, 3/4 and 1) from the original dataset.
Betchkal also listens to a subsample of the recordings.
