Sentence examples for used a random forest from inspiring English sources

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).

This study used a random forest machine learning approach to estimate daily maximum and minimum air temperatures (Tmax and Tmin) for two megacities with different climate characteristics: Los Angeles, USA, and Seoul, South Korea.

Similarly, Rehfeldt et al. (2014) used a random forest classification model to project ponderosa pine and Douglas-fir climate niche space through 2060 under the RCP60 (medium-high) emissions scenario, and predicted a net gain for ponderosa pine in the central Rocky Mountains (particularly in the central Idaho region), while projecting a loss of Douglas-fir habitat at lower elevations.

We next used a random forest classifier (Breiman, 2001) to predict which of the 92 HSC1 single-cell RNA-seq profiles have a molecular signature similar to the intersecting MolO subprofileson identified in Figure 2C.

To provide a better prediction, Tastan et al.[ 63] applied a method combining multiple data sources, and used a random forest classifier to predict interactions between HIV-1 virus and human proteins.

RFCRYS used a random forest classifier [ 11] including predicted surface ruggedness, hydrophobicity, side-chain entropy of surface residues and amino acid composition of the predicted protein surface to improve the prediction of crystallization success [ 12].

In this study we used a Random Forest-based approach for an assignment of small guanosine triphosphate proteins (GTPases) to specific subgroups.

We used a random forests model to determine which variables were most predictive of plaque progression, and the most significant cutpoints to dichotomize each variable.

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).