Integrated Prediction using Uni-Variate and Multivariate Random Forests
Prediction using Random Forest or Multivariate Random Forest
Model of a single tree of Random Forest or Multivariate Random Forest
Weights for combination of predictions from different data subtypes us...
Integrated Prediction of Testing samples using Combination Weights fro...
Prediction for testing samples using specific combination weights from...
Generate training and testing samples for cross validation
Error calculation for integrated model
Imputation of a numerical vector
Integrated Prediction of Testing samples from integrated RF or MRF mod...
Information Gain
Prediction of testing sample in a node
Prediction of Testing Samples for single tree
Splitting Criteria of all the nodes of the tree
Split of the Parent node
An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.