rf_viz | R Documentation |
The Input Data, Local Importance Scores, and Classic Multidimensional Scaling Plots
rf_viz(rfprep, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = "orange")
rfprep |
A list of prepared Random Forests input data to be used in visualization, created using the function rf_prep. |
input |
Should the Input Data Parallel Coordinate Plot be included in the visualization? |
imp |
Should the Local Importance Scores Parallel Coordinate Plot be included in the visualization? |
cmd |
Should the Classic Multidimensional Scaling Proximites 3-D XYZ Scatter Plot be included in the visualization? |
hl_color |
The highlight color when you select points on the plot(s). |
Any combination of the parallel coordinate plots of the input data, the local importance scores, and the 3-D XYZ classic multidimensional scaling proximities from the output of the random forest algorithm.
For instructions on how to use randomForests, use ?randomForest. For more information on loon, use ?loon.
For detailed instructions in the use of these plots in this package, visit https://github.com/chriskuchar/rfviz/blob/master/Rfviz.md
Chris Kuchar chrisjkuchar@gmail.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
randomForest
, rf_prep
, l_plot3D
, l_serialaxes
#Classification with iris data set rfprep <- rf_prep(x = iris[,1:4], y = iris$Species) #View all three plots Myrfplots <- rf_viz(rfprep, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange') #Select data on any of the plots then run: iris[Myrfplots$input['selected'], ] iris[Myrfplots$imp['selected'], ] iris[Myrfplots$cmd['selected'], ] #Rotate 3-D XYZ Scatterplot #1. Click on 3-D XYZ Scatterplot #2. Press 'r' on keyboard to enter rotation mode #3. Click and drag mouse to rotate plot #4. Press 'r' to leave rotation mode #View only the Input Data and CMD Scaling Proximities Plots Myrfplots <- rf_viz(rfprep, input = TRUE, imp = FALSE, cmd = TRUE, hl_color = 'orange') #Regression with mtcars data set rfprep2 <- rf_prep(x = mtcars[,-1], y = mtcars$mpg) #View all three plots Myrfplots <- rf_viz(rfprep2, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange') #Unsupervised clustering with iris data set rfprep <- rf_prep(x = iris[,1:4], y = NULL) #View the Input Data and CMD Scaling Proximities Plots for the unsupervised case. #(Importance Scores Plot not valid here) Myrfplots <- rf_viz(rfprep, input = TRUE, imp = FALSE, cmd = TRUE, hl_color = 'orange')
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