Decision Forest
Performance evaluation from other modeling algorithm Result
Construct Decision Tree model with pruning
QSAR dataset with DILI endpoint for demo
Performance evaluation from Decision Tree Predictions
T-test for feature selection
Decision Forest algorithm: confidence level accumulated plot
Decision Forest algorithm: confidence level accumulated plot (accumula...
Decision Forest algorithm: Model training with Cross-validation
output summary for Dforest Cross-validation results
Decision Forest algorithm: Feature Selection in pre-processing
Decision Forest algorithm: Data pre-processing
Simple pre-defined pipeline for Decision forest
performance evaluation between two factors
Decision Forest algorithm: Model prediction
Decision Forest algorithm: Model training
output summary for Dforest test results
Demo script to lean Decision Forest package Demo data are located in d...
multiplot
Doing Prediction with Decision Tree model
Provides R-implementation of Decision forest algorithm, which combines the predictions of multiple independent decision tree models for a consensus decision. In particular, Decision Forest is a novel pattern-recognition method which can be used to analyze: (1) DNA microarray data; (2) Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) data; and (3) Structure-Activity Relation (SAR) data. In this package, three fundamental functions are provided, as (1)DF_train, (2)DF_pred, and (3)DF_CV. run Dforest() to see more instructions. Weida Tong (2003) <doi:10.1021/ci020058s>.