Rfviz is an interactive package and toolkit in R, using TclTK code on the backend, to help in viewing and interpreting the results Random Forests for both Supervised Classification and Regression in a user-friendly way.
Currently, rfviz implements the following statistical graphs, with functions to view any combination of the plots:
The three plots are:
1. The classic multidimensionally scaled proximities are plotted as a 2-D XYZ scatterplot.
2. The raw input data is plotted in a parallel coordinate plot.
3. The local importance scores of each observation are plotted in a parallet coordinate plot.
rfviz is built using the package Loon on the backend, and implements the random forests algorithm.
For detailed instructions in the use of these plots in this package, visit https://github.com/chrisbeckett8/rfviz/blob/master/Rfviz.md
For instructions on how to use randomForests, use ?randomForest. For more information on loon, use ?loon.
Chris Beckett email@example.com, based on original Java graphics by Leo Breiman and Adele Cutler.
Liaw A, Wiener M (2002). “Classification and Regression by randomForest.” _R News_, *2*(3), 18-22. https://CRAN.R-project.org/doc/Rnews/
Waddell A, Oldford R. Wayne (2018). "loon: Interactive Statistical Data Visualization" https://github.com/waddella/loon
Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.
Breiman, L (2002), “Manual On Setting Up, Using, And Understanding Random Forests V3.1”, https://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf
Breiman, L., Cutler, A., Random Forests Graphics. https://www.stat.berkeley.edu/~breiman/RandomForests/cc_graphics.htm
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