… A dataset of VD values for 384 drugs in humans was used to train a hybrid mixture discriminant
analysis−random forest (MDA-RF) model using 31 computed descriptors. Descriptors …

… behind the random forest methodology. We present the discriminant random forest approach
… the parametric multivariate discrimination technique called linear discriminant analysis. The …

… When taking into account sensitivity, specificity and overall classification accuracy
Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in …

… over bagging by using a random small tweak that … , random forests overcome this problem
by forcing each split to consider only a subset of the predictors. In addition, random forests use …

… With the combination of random forest and NPD feature proposed in [2], we achieved more
than 30 FPS. In validation and recognition stage, we used Linear Discriminant Analysis to …

… DART‐TOFMS spectra obtained from wood slivers and post‐processing analysis using KDA
and Random Forest classification separated Pericopsis elata from the other Pericopsis taxa …

… Tree, Random Forest and Linear Discriminant Analysis respectively … The results confirm that
the Random Forest model can … However, the Linear Discriminant Analysis model can keep …

… A Random Forest classifier does not suffer from overfitting as the number of the predictor …
classification is random and hard to interpret which is why we did not use Random Forest as …

… , linear discriminant analysis (LDA), and random forest (RF), have been successfully used
in metabolomics, their performance including strengths and limitations in clinical data analysis …

… A random forest (RF) classifier is an ensemble classifier that produces multiple decision
trees, using a randomly selected subset of training samples and variables. This classifier has …