Hello,
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: https://stats.stackexchange.com/questions/168964/building-a-regression-tree-with-r-from-scratch/168967#168967 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: https://cran.r-project.org/web/packages/forestRK/index.html (CRAN) https://github.com/h56cho/forestRK (Github) Hyunjin Cho [[alternative HTML version deleted]]
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