ODRF (development version)
Added linear model tree. Specifically, use parameter Xsplit as the splitting variable for ODT and fit a linear model for each split using the function “gmlnet”. The corresponding parameter is split=“linear”.
Added a classification and regression model tree. Changed the parameter “leafnode” to “type” in the function predict.ODT(), and for classification tasks, added category probability output.
Optimized some other known issues.
ODRF 0.0.4
CRAN release: 2023-05-28
- Fixed function VarImp(), adding the method of measuring the importance of variables with node purity, and now VarImp() can be used for both class ODT and ODRF.
- When the argument “Xcat ! = 0”, i.e., the category variable in predictor X is transformed to one-of-K encode. however for the argument “NodeRotateFun=‘RotMatRF’ (‘RotMatRand’)“ run error, we have now fixed it.
- Added predicted values of training data for class ODT and ODRF.
- Fixed issue related to function predict.ODRF() when argument “weight.tree = TRUE”.
- Optimized some other known issues.
ODRF 0.0.3
CRAN release: 2023-03-16
- The function predicate.ODT() runs error when ODT is not split (depth=1), and we have fixed this bug.
- We have fixed the function predict.ODRF() with arguments numOOB and weight.tree related issues.
- We have fixed the functions plot.ODT(), VarImp() and plot.VarImp().
- We have fixed the argument ‘lambda’ of the functions ODT() and ODRF().
ODRF 0.0.2
CRAN release: 2023-02-28
- We have now explained CART and Random Forest in the description text.
- We have changed the Date field to a more recent date.
- We have now exported the functions RandRot() and defaults(), and no longer need ODRF:::
- We have removed par from plot.VarImp() and added on.exit to plot.prune.ODT(), and checked the code to make sure that it does not change the user’s options, including par or working directory.
- We have removed the random seed number in functions ODRF(), poune.ODRF(), online.ODRF() and plot_ODT_depth().