Oblique Decision Random Forest for Classification and Regression
Projection Pursuit Optimization
predict based on an ODRF object
making predict based on ODT objects
print ODRF
print ODT result
Pruning of class ODRF.
pruning of class ODT
prune ODT or ODRF
Samples a p x p uniformly random rotation matrix
Create rotation matrix used to determine the linear combination of fea...
Create a Projection Matrix: RotMatPPO
Random Rotation Matrix
Create a Projection Matrix: Random Forest (RF)
Extract variable importance measure
accuracy of oblique decision random forest
ODT as party
find best splitting variable and node
Default values passed to RotMat*
Classification and Regression using the Ensemble of ODT-based Boosting...
Classification and Regression using Oblique Decision Random Forest
Classification and Regression with Oblique Decision Tree
using new training data to update an existing ODRF.
using new training data to update an existing ODT.
online structure learning for class ODT and ODRF.
Pipe operator
plot oblique decision tree depth
plot method for Accuracy objects
to plot an oblique decision tree
to plot pruned oblique decision tree
Variable Importance Plot
The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) <DOI:10.1201/9781315139470> and Random Forest of Breiman (2001) <DOI:10.1023/A:1010933404324> respectively.