Creating Clearer Visualizations for a Four-Class Decision Tree in R Using rpart and rattle

In the provided R code, a decision tree is generated using the rpart and caret packages, and visualized with the rattle package. The resulting plot displays a four-class decision tree, but due to the complexity, it is challenging to interpret. I seek advice on improving clarity by creating individual trees for each class, allowing for a more focused and comprehensible presentation.

library(rpart)
library(caret)

fitControl <- trainControl(method = "repeatedcv",
                           number = 10,
                           repeats = 10)

classifier = train(x = training_set[, names(training_set) != "Target"],
                   y = training_set$Target,
                   method = 'rpart',
                   parms = list(split = "gini"), trControl = fitControl,
                   tuneLength = 20)
classifier
complexity_parameter=classifier$bestTune

classifier = rpart(formula = Target ~ .,
                   data = training_set,parms = list(split = "information"),
                   control = rpart.control(cp = complexity_parameter))


library(RColorBrewer)
library(rattle)
fancyRpartPlot(classifier, caption = NULL, clip.right.labs=FALSE,branch=.3,type=3,
               tweak=1.4)

I have a four-class decision tree, and as shown in the attached figure, the visualization is intricate and hard to decipher. I am considering drawing four separate trees, each dedicated to one class (e.g., the first tree displaying only leaves of the first class, the second tree for the second class, and so forth). I would appreciate any guidance or code snippets on how to achieve this and improve the interpretability of the decision tree plots

Edit. This is a sample of the training dataset:

training_set<- structure(list(AGE67CIYes = c(-0.176152387930331, -0.987016328202176, 
0.05552302357591, -0.58468873762319, 0.162742606800352, 0.120896778307869, 
-0.987016328202176, -0.64359160055763, -0.987016328202176, -0.628598979432629, 
-0.987016328202176, -0.307993987241449, 0.889554504998379, -0.987016328202176, 
-0.84077646366108, -0.122806076070342, 0.347797256654688, -0.585917218815798, 
3.27330664446935, -0.227710210722183, -0.987016328202176, 0.0907486763211531, 
-0.468831088265139, -0.0852317172820009, 2.14649177996699, -0.21957742854859, 
-0.478526166947832, -0.987016328202176, 0.856614535142944, -0.987016328202176, 
0.233732369261435, 0.773841021986012, 1.76557884040399, 1.70409677446699, 
0.177204736845891, -0.987016328202176, -0.0303666544722618, 0.267016686824448, 
-0.987016328202176, 0.133602302476064, -0.780150252101327, 0.569019137931335, 
0.54169474123801, 1.04432323350976, -0.00304660292847676, 0.595633772087449, 
0.0119187160870928, -0.987016328202176, 0.445153602815489, -0.0754726273166524, 
-0.0553854181026097, -0.987016328202176, 0.447212111288207, -0.412974267062895, 
0.565701855297101, 0.0332551927612325, 0.61493438306659, -0.987016328202176, 
-0.245916422283762, 0.0936414642259315, 0.217200249252726, -0.774974426145616, 
2.01102787070915, 0.644784396320045, 1.31792583076954, 0.0693891516694634, 
0.152242180258608, 1.09469958100705, 1.13548440454805, -0.158246875053666, 
0.755736021038804, 0.672766538708062, -0.735850059896174, -0.987016328202176, 
0.145397105625745, 1.03910128090896, -0.987016328202176, -0.987016328202176, 
-0.404673257734619, 0.215913693080231, 0.388480617599278, 0.411918265238067, 
-0.987016328202176, 0.113253215693915, 0.334391574463053, -0.558854795203353, 
1.27293994403935, 0.429900076191951, -0.535607536710634, 1.15212162607829, 
1.49001293895707, -0.987016328202176, 0.886209458949893, -0.11303124287923, 
3.37348021367463, 0.735737223588497, -0.0476125737990377, -0.14765557213803, 
-0.481299453037445, -0.0487309116018985), AGE18_34M = c(-0.311288013988282, 
-0.920252005474412, 0.839075436208503, 0.475468979668509, 0.361813050743929, 
-0.0880491662273217, -1.30060058115752, 0.2381797045763, 0.46997486395285, 
0.0951842010198588, -0.249345366669994, 0.0276151129841655, -0.679404318051254, 
-0.460158578131396, -0.0580351642511107, -0.4914107121112, 0.0350108362887032, 
-0.726823880120434, -1.21665514019721, -0.665041398673074, 2.03127028459426, 
0.173414240816047, -1.06412941985671, -0.379467449594911, 0.971659698265082, 
3.58247384148793, 0.0419848907173112, -0.122469737335293, 1.77830824717609, 
-0.514049917080521, -0.18293712720485, 2.46932240611204, 0.103057098341037, 
0.416637231962104, 0.0347935126703198, -0.0683916002418135, -0.140487578187558, 
0.568918482749401, 0.80596534428402, 0.709398516775547, -0.894621921017203, 
0.0649799930004623, -0.548245672308119, 1.44034787873404, 0.358249364890296, 
2.26940685862186, -0.268422759950531, 1.5743831289976, -0.532610474943992, 
-0.348407548366379, 0.853760927062079, -0.291156653609838, -0.243149894243735, 
-0.844996329920804, 0.648164055112016, 0.273498687490394, 1.25994011310977, 
-0.792349093161483, 1.1447573459646, -0.013918010591892, -0.526596681137413, 
-0.658346817606973, 0.516140362965466, -0.78849760892925, -0.554157331051845, 
0.243647599042001, 2.04373725057226, -0.97656446859546, -0.912356887137583, 
-0.824415433098142, -0.335325459216275, -0.587458300768377, -0.258938778587358, 
-0.555568876651955, 4.63824989974633, -0.412721089416945, 1.00499324152535, 
-0.306301918538513, -0.228161687409781, -0.129884546841866, -0.0114026911643898, 
1.87550512616216, -0.944634183197748, -0.262575555185161, 0.861798664854366, 
-0.0509189201758156, -0.612609020943053, 0.110701490753568, -0.726367609420112, 
-0.375128625430529, -0.99933686605059, -0.19110078784832, -0.371574440790039, 
-0.938132768287129, 0.243368554114898, 0.929595838846939, -0.88523573776942, 
-0.810391389504971, -0.331438750074501, 0.873586339714407), AGE67 = c(0.501041434601308, 
-1.09665902567313, -0.499366515285846, -1.89627995143666, -0.298655565821342, 
-0.657227918269277, 0.738638322699508, 0.844563870334618, -1.56421059601705, 
-0.0241556789265915, -0.641594843326121, -0.478951705500202, 
2.61976255704089, 1.96434823104864, -1.22122015725059, 1.55163432276405, 
1.27827046071975, -0.343864172693654, 3.31198498838896, 1.66372654109949, 
0.37030630478651, -0.419408311079017, -0.555972438485822, -0.774936554113457, 
0.107872632262183, -1.34044707928847, -0.839545557277716, 1.92619155286728, 
0.57973553068234, 0.221251081710207, -0.163884033780637, -0.687948777957268, 
-0.0113182539359213, -0.37327476423973, -1.27171923730041, -1.38681856755255, 
-1.40429288028817, 0.168119278247155, -0.659256439010892, -1.07437656281392, 
-0.696986471528318, -0.864024735676032, -0.534449400062125, -0.180293103433951, 
2.30163523766365, 3.15498511491188, 0.307283716170217, -0.157204374134115, 
-0.518770979831071, -0.401045752552977, 0.997254420383102, 0.232181798069135, 
-0.405998845074965, -0.568631832646478, 0.254930436291304, 0.404278675370718, 
0.392765574171448, 2.47506069286078, -1.8557905876778, -0.0631740179401674, 
0.0298313302450834, -0.647883494529081, -0.207182743392638, -1.39352452511716, 
-1.34117850614179, 0.944792602443884, -1.24991896454826, -1.86690843412548, 
-0.905681536580039, 0.665146208715289, -0.322988621135965, 0.014606612397604, 
-0.911460946841895, -0.218306616580415, 0.26710983425054, 0.514703818235693, 
2.14113331398361, -0.616093681265326, 0.0291501450665863, -0.44680387231444, 
-0.25559820550726, -0.486781511123927, 1.46702523452746, 2.14512158886705, 
0.586724522901828, -0.354832348471152, -0.0891185777716379, 0.684009304312411, 
-0.946069291878499, -0.730270080428137, -0.101775097076054, 1.62556716185319, 
0.37030630478651, -0.819482083082821, 1.64647740419461, -0.556561969797837, 
0.0432415212999982, 0.279105885015719, -0.719800485475731, 0.352766684885891
), AGE67IT = c(0.546552412166264, -1.07526365158326, -0.468955640670514, 
-1.8974512238005, -0.310213066218594, -0.648052048503732, 0.787735574714999, 
0.854938313505668, -1.54987241979056, -0.0707358644941545, -0.726319975489725, 
-0.475290635689973, 2.4034814230678, 2.03194628146796, -1.20170490207676, 
1.61300289199568, 1.25342092240672, -0.311106154213347, 3.34434571314056, 
1.72678702415178, 0.413843973443276, -0.423068606116984, -0.526415970583246, 
-0.795742413966101, 0.0958131387143464, -1.34275483486837, -0.847437129878995, 
1.85139271626387, 0.555660476058683, 0.188567559007975, -0.160260210148408, 
-0.76145871420041, 0.02645919447723, -0.384844658381296, -1.27679963538867, 
-1.36980284573822, -1.38754091479577, 0.208605146969538, -0.669199963819832, 
-1.07508354694689, -0.669558461737779, -0.845642806091824, -0.50456806459325, 
-0.145066152169093, 2.29730541056113, 3.24055457341925, 0.271679866495853, 
-0.173297360212068, -0.50168800913321, -0.399729324986892, 0.977333088835057, 
0.27363462296638, -0.404248928734908, -0.574213826955535, 0.272419254286123, 
0.408398950123785, 0.436642241813503, 2.55036740928169, -1.84585350391826, 
-0.0355778324729171, 0.0525201570882496, -0.619714327085901, 
-0.182564609086414, -1.38913227212412, -1.32347389573356, 0.952476863305029, 
-1.23894360432029, -1.86146509438973, -0.923463257162492, 0.648263392221682, 
-0.358121272963515, -0.00290343411139714, -0.887270203066352, 
-0.205933003746037, 0.309089861018009, 0.520773126763273, 2.11253418114413, 
-0.58744468011722, -0.0301375036843241, -0.425015388384307, -0.228529543651722, 
-0.456180679143214, 1.29606011127543, 2.1676026243595, 0.633528796321265, 
-0.342348134083138, -0.0525154431967221, 0.686070667402912, -0.922400924572503, 
-0.716563246171896, -0.0653629993121732, 1.68805166931954, 0.413843973443276, 
-0.82126700480962, 1.66661355031845, -0.527014400248682, 0.0573323154964607, 
0.255567177377771, -0.732301118039339, 0.396039611477956), DWE1tot = c(-1.24627225843832, 
-0.972515585869268, -0.591117336947849, -0.950950893751152, -0.00341326678369597, 
-0.317703674167421, -1.01860592322038, 1.70500797025228, -2.80147690897104, 
0.0841927598217607, -2.31467874579201, -0.634564452794059, 1.63477150881602, 
2.39176448207346, -0.603097208459657, 0.554218462480144, 0.596025520656533, 
-0.363363481034292, 0.326011018689958, 1.45302282344243, 0.259839566760795, 
-0.323873062136694, -0.645196692893364, 0.126535915869014, 0.273354029798474, 
-1.6765246798592, -0.858119384992372, 0.559696169973685, 0.965787728534269, 
0.168732755000225, -0.105465993392139, -0.476904337564692, 0.158116000800885, 
0.368839813382008, -1.53101094871713, -0.768492476757933, -0.580381980928888, 
0.383757877076662, -2.51125311699879, -0.285564675616673, -0.307208273135031, 
-0.1958624767505, -0.52862437997276, -1.09793370211631, 2.37364271507784, 
1.94326659579375, -0.29069268473587, -1.3926121391431, -0.454695942608588, 
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-0.4279250695409, -0.641299416040736, 0.211479245514941, -0.594712699321882, 
2.85291296625091, -0.317088857444325, 0.30614248023326, -0.375126422991232, 
-0.631092891479471, -0.286277701436183, -1.74098962562517, -1.24105303470107, 
0.709126059783464, -0.915573987456023, -2.33328457590235, -1.24384520039966, 
0.371696888960207, 0.0337244463841061, 0.718542081472043, -1.15989953982117, 
-0.249183962565405, -0.457050613250607, 0.543066989542432, 1.55057780183794, 
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-0.0184323959900459, 2.93015633378211, 2.52801762478007, 0.435292040053886, 
-0.373077593601094, 0.295243869739579, 0.425845049612918, -0.475620953880973, 
-1.10368797436764, 0.346155709987028, 0.434151223026415, 0.632305650134888, 
-0.499317186271939, 1.66284666421724, -0.464006541934083, -0.395742174424708, 
0.0804687862477327, -0.714833790842392, 0.529197578800853), AGE18_34NoIT = c(0.19940732465257, 
-0.46709113395183, 2.34119335826173, -0.287924912209743, -0.767850884074149, 
-0.107050721676688, -0.767850884074149, 0.358720441384029, 0.30950776246791, 
0.94233552589833, 0.0931152138034797, 0.47658498239766, -0.767850884074149, 
-0.767850884074149, 0.671325955294912, -0.767850884074149, 0.369480488190256, 
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0.225596235651883, -0.767850884074149, -0.767850884074149, -0.472755159377862, 
2.58882706714496, 1.12766318625321, 0.312810011192943, 2.00670978225976, 
-0.486023852035069, 0.136757539237998, 1.68270945706995, -0.767850884074149, 
0.970984615352628, 1.24052217326025, -0.510972474543447, 0.729924457795809, 
0.54106416210258, 0.677682601292349, 1.00412854244753, 0.21920946120918, 
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-0.767850884074149, 0.233400424639771, 0.137033599931982, -0.20822292045369
), AGE67CINo = c(0.520215683696846, -1.03163833351555, -0.509119810566456, 
-1.87130724349216, -0.314585715658465, -0.673792033984295, 0.823598776742872, 
0.903906066078584, -1.50426966218324, 0.02457959376149, -0.57163006325451, 
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1.5780646265547, 1.26504842710326, -0.301929339443148, 3.09282231729659, 
1.69955169568212, 0.45126498073184, -0.431038644185776, -0.52546864588487, 
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2.0240542914681, 0.519263167059362, 0.300590333065505, -0.18388341953198, 
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-0.819574921820783, 1.40141138963966, -0.619957661890062, 0.0474226036519372, 
0.293647638969982, -0.690104740437684, 0.360397804324386), AGE67CAR2 = c(-0.65814805058186, 
-0.65814805058186, -0.65814805058186, 0.00768964415182037, -0.65814805058186, 
1.19533826054216, -0.65814805058186, -0.231881369541573, -0.65814805058186, 
0.231603684740978, 0.237714616989074, -0.397640616985576, 4.000337820787, 
2.0821377561057, -0.476631762802389, 0.414529617167548, 0.584452971176845, 
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training_set$Target<-structure(c(2L, 3L, 1L, 4L, 4L, 3L, 1L, 1L, 3L, 3L, 3L, 4L, 3L, 
4L, 4L, 2L, 4L, 3L, 3L, 2L, 3L, 3L, 4L, 1L, 2L, 4L, 2L, 3L, 1L, 
3L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 4L, 2L, 3L, 3L, 2L, 4L, 2L, 2L, 
3L, 2L, 1L, 1L, 4L, 2L, 3L, 4L, 3L, 4L, 2L, 2L, 3L, 4L, 1L, 2L, 
1L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 1L, 3L, 2L, 2L, 3L, 4L, 4L, 1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 3L, 2L, 
3L, 1L, 3L, 4L, 4L, 2L, 4L), levels = c("Q1", "Q2", "Q3", "Q4"
), class = "factor")

This is a complex problem. It is probably easiest to convert your rpart to an igraph object. This will allow you to select subgraphs that end in a particular class.

Step 1: Reproducing the problem

The sample data does not produce a decision tree, so was not sufficient to allow the problem to be reproduced. However, we can easily create something similar using:

training_set <- expand.grid(A = 1:4, B = 1:4, C = 1:4, D = 1:4)

training_set$Target <- cut(rowSums(training_set), 
                           breaks = c(0, 8, 10, 12, 20),
                           labels = c('w', 'x', 'y', 'z'))

This now allows us to use your own code to create the classifier object:

library(rpart)
library(caret)

fitControl <- trainControl(method = "repeatedcv",
                           number = 10,
                           repeats = 10)

classifier = train(x = training_set[, names(training_set) != "Target"],
                   y = training_set$Target,
                   method = 'rpart',
                   parms = list(split = "gini"), trControl = fitControl,
                   tuneLength = 20)
classifier
complexity_parameter=classifier$bestTune

classifier = rpart(formula = Target ~ .,
                   data = training_set,parms = list(split = "information"),
                   control = rpart.control(cp = complexity_parameter))

We now get a similar legibilty problem with the output of the fancy rpart plot:

library(RColorBrewer)
library(rattle)
fancyRpartPlot(classifier, caption = NULL, clip.right.labs=FALSE,branch=.3,type=3,
               tweak=1.4)

Step 2: Converting rpart to igraph

I couldn't find an existing method for converting an rpart to an igraph binary tree. The data.tree package allows for rpart to Node to igraph, but the end result is not a binary tree.

The method I have used here is to create a binary tree in igraph, copy over the node attributes and remove vertices that are missing from the rpart object:

library(igraph)

df <- classifier$frame
nodes <- as.numeric(row.names(df))
non_nodes <- setdiff(seq(max(nodes)), nodes)

g <- graph.tree(max(nodes), mode = 'out')
labs <- ifelse(df$var == '<leaf>', 
               levels(training_set$Target)[df$yval], 
               labels(classifier))
classed <- ifelse(df$var == '<leaf>', df$yval, NA)
vertex_attr(g, 'name') <-  labs[match(V(g), nodes)]
vertex_attr(g, 'number') <- df$n[match(V(g), nodes)]
vertex_attr(g, 'class') <- as.character(classed[match(V(g), nodes)])
g <- delete.vertices(g, non_nodes)

Step 3: Ensure our igraph is correct by plotting with ggraph

We will plot the igraph using ggraph to check it is correct. Note that we still run into legibility problems because we have not yet converted to subgraphs:

library(ggraph)

ggraph(g, layout = 'tree') +
  geom_edge_diagonal() +
  geom_node_label(aes(label = paste(name, number, sep = '\n n = '),
                      fill = class)) +
  scale_fill_manual(values = c(`1` = 'lightgreen', `2` = 'lightblue',
                               `3` = 'orange', `4` = '#E0A8FF'),
                    na.value = 'white', guide = 'none') +
  theme_graph()

Step 4: Getting subgraphs that end in each class

This involves obtaining the subcomponents that represent paths to each node containing a target class, then getting the induced subgraph:

subs <- lapply(levels(training_set$Target), function(n) {
  which(V(g)$name == n) |>
  lapply(function(x) subcomponent(g, x, 'in')) |>
  unlist() |>
  unique()})

subs <- lapply(subs, function(x) {
  induced.subgraph(g, x)
})

Step 5: plotting the result

This is really just replicating our plotting code above for each subgraph. We put them in a list for convenience:

plots <- lapply(subs, function(x) {
  ggraph(x, layout = 'tree') +
    geom_edge_diagonal() +
    geom_node_label(aes(label = paste(name, number, sep = '\n n = '),
                        fill = class)) +
    scale_fill_manual(values = c(`1` = 'lightgreen', `2` = 'lightblue',
                                 `3` = 'orange', `4` = '#E0A8FF'),
                      na.value = 'white', guide = 'none') +
    theme_graph()
})

Now we have:

plots[[1]]

plots[[2]]

plots[[3]]

plots[[4]]

We can see that this has given us the correct subtrees with the correct partition paths. The displayed information at each node is customizable - it is just a case of copying it over from rpart to igraph at step 2.