Sentence examples for using a random forest from inspiring English sources

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.

With the training set, we build a first person tweet classifier for each topic using a Random Forest.

We have combined these features using a Random Forest classifier [13] with kernel discriminant analysis using spectral regression (SR-KDA).

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.

Following the detection of change, the land cover is mapped using a random forests classifier.

Our analysis builds on 28 Landsat scenes from 1985 to 2011 that have been classified using a random forests approach.

We built the oil and gas model using a Random Forests model with 300 bootstrap replicates or classification trees (k) and using the entire sample dataset for out-of-bag (OOB) testing with replacement.

MutPred (Li et al., 2009) evaluates the probabilities of gain or loss of structure and function upon mutations and predicts their impact using a Random Forest-based approach.

By regressing known postmortem interval directly on the taxon relative abundances using a Random Forests model.