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The motivation for using random forests as a combiner is motivated by the COBRA approach.
We use random forest as this classifier implicitly handles the uncertainty in the position measurements.
We mapped conifer stand characteristics using spatial wavelet analysis, and modeled lek activity as a function of conifer-related and additional lek site covariates using random forests.
Pathway analysis using random forests classification and regression.
Features are combined using random forests and kernel discriminant analysis.
As 29 markers were too many for one algorithm, the feature selection process, which reduced biomarkers included in one algorithm, was performed using random forests (RF).
Random forest MICE produced narrower 95% confidence intervals for the x3 coefficient than parametric MICE (P < 0.001), and coverage was only 80% using parametric MICE as compared with 95% using random forest MICE with 5 100 trees.
The prediction models were created using both logistic models and random forest models, as some might argue that using random forest imputation methods might favour the random forest prediction models.
Simulations using random forest probability machines are presented.
QSAR methodologies used Random Forests and Associative Neural Networks.
Second, we use random forests.
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