rf_viz {rfviz}

Random Forest Plots for interpreting Random Forests output

Description

The Input Data, Local Importance Scores, and Classic Multidimensional Scaling Plots

Usage

rf_viz(rfprep, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = "orange")

Arguments

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).

Value

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.

Note

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

Author(s)

Chris Kuchar [e-mail], based on original Java graphics by Leo Breiman and Adele Cutler.

References

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

See Also

randomForest, rf_prep, l_plot3D, l_serialaxes

Examples

#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')