NEWS.md

In ODRF: Oblique Decision Random Forest for Classification and Regression

ODRF 0.0.4

• 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

• 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

• 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().

ODRF 0.0.1

• Added a NEWS.md file to track changes to the package.
• This is the first fully-functioning version of the package. It currently has no ERRORs, WARNINGs, or NOTEs from devtools::check().

ODRF documentation built on May 31, 2023, 8:22 p.m.