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By default, Trainable Weka Segmentation uses a random forest algorithm [30], but another may be chosen if the operator desires.
Here we present "Elucidating Network Topology with Sequence" (ENTS), a binary PPI classifier that uses a random forest framework.
The MV method [ 9] integrates a wide range of structural, evolutionary, energy-based and experimental data and uses a random forest method to predict functional sites, including protein-, peptiDNA-,DNA-, RNA-binding sites on protein structures.
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Using a random forest model allows us to get out-of-bag (OOB) estimates which are akin to cross-validation and are useful for estimating the performance of models created using small datasets.
When using a random forest, a feature vector is needed.
We have combined these features using a Random Forest classifier [13] with kernel discriminant analysis using spectral regression (SR-KDA).
With the training set, we build a first person tweet classifier for each topic using a Random Forest.
Using a random forest as a global regression tool, we found a coefficient of determination of 0.561 in a ten-fold cross-validation.
Classification of data using a Random Forest simply involves traversal of many decision trees, which can be multithreaded easily for fast computation on multi-core processors.
To map carbon distributions as well as to analyze the relation between phenological parameters and aboveground carbon values, we used a random forest regression (RFR).
In this paper, we evaluate the potential of these technologies to map 15 common urban tree species using a Random Forest (RF) classifier in the City of Surrey, British Columbia, Canada.
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