I am pleased to announce my new package forestRK. The package implements Forest-RK algorithm discussed in the paper "Forest-RK: A New Random Forest Induction Method" by Simon Bernard, Laurent Heutte, Sebastien Adam, 4th International Conference on Intelligent Computing (ICIC), Sep 2008, Shanghai, China, pp.430-437, to various datasets for classification.
Some of the forestRK functions were built based on the discussion:
Examples of functions included in the new forestRK package are (there are 17 functions in total in this package):
1. construct.treeRK: Builds a single decision tree after implementing the RK (random �K�) algorithm (i.e. builds �rktree�);
2. pred.treeRK: Makes predictions on the test observations based on the �rktree� model in question;
3. draw.treeRK: Makes igraph plot of a �rktree�.
4. forestRK: Builds a Forest-RK model;
5. pred.forestRK: Makes predictions on the test observations by using the Forest-RK algorithm;
6. mds.plot.forestRK: Generate 2D Multi-Dimensional Scaling plot of the test observations, where the test observations are colour coded by their predicted class type indicated in the pred.forestRK object;
7. importance.forestRK: Calculate Gini Importance of each covariate based on a forestRK model;
8. importance.plot.forestRK: Generate Importance ggplot of the covariates.
The forestRK package also provides tools to encode non-numeric dataset into a numeric one via Numeric Encoding or Binary Encoding.
For more information about the new forestRK package, please visit: