• using R Under development (unstable) (2026-07-23 r90295)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)     GNU Fortran (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)
• running under: Fedora Linux 44 (Server Edition)
• using session charset: UTF-8 * current time: 2026-07-24 07:20:44 UTC
• using option ‘--no-stop-on-test-error’
• checking for file ‘RWeka/DESCRIPTION’ ... OK
• this is package ‘RWeka’ version ‘0.4-49’
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• checking whether package ‘RWeka’ can be installed ... OK See the install log for details.
• checking package directory ... OK
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• checking sizes of PDF files under ‘inst/doc’ ... OK
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• checking examples ... ERROR Running examples in ‘RWeka-Ex.R’ failed The error most likely occurred in: > ### Name: Weka_classifier_trees > ### Title: R/Weka Classifier Trees > ### Aliases: Weka_classifier_trees J48 LMT M5P DecisionStump plot.Weka_tree > ### parse_Weka_digraph > ### Keywords: models regression classif tree > > ### ** Examples > > m1 <- J48(Species ~ ., data = iris) > > ## print and summary > m1 J48 pruned tree ------------------ Petal.Width <= 0.6: setosa (50.0) Petal.Width > 0.6 | Petal.Width <= 1.7 | | Petal.Length <= 4.9: versicolor (48.0/1.0) | | Petal.Length > 4.9 | | | Petal.Width <= 1.5: virginica (3.0) | | | Petal.Width > 1.5: versicolor (3.0/1.0) | Petal.Width > 1.7: virginica (46.0/1.0) Number of Leaves : 5 Size of the tree : 9 > summary(m1) # calls evaluate_Weka_classifier() === Summary === Correctly Classified Instances 147 98 % Incorrectly Classified Instances 3 2 % Kappa statistic 0.97 Mean absolute error 0.0233 Root mean squared error 0.108 Relative absolute error 5.2482 % Root relative squared error 22.9089 % Total Number of Instances 150 === Confusion Matrix ===   a b c <-- classified as  50 0 0 | a = setosa   0 49 1 | b = versicolor   0 2 48 | c = virginica > table(iris$Species, predict(m1)) # by hand                           setosa versicolor virginica   setosa 50 0 0   versicolor 0 49 1   virginica 0 2 48 > > ## visualization > ## use partykit package > if(require("partykit", quietly = TRUE)) plot(m1) > ## or Graphviz > write_to_dot(m1) digraph J48Tree { N0 [label="Petal.Width" ] N0->N1 [label="<= 0.6"] N1 [label="setosa (50.0)" shape=box style=filled ] N0->N2 [label="> 0.6"] N2 [label="Petal.Width" ] N2->N3 [label="<= 1.7"] N3 [label="Petal.Length" ] N3->N4 [label="<= 4.9"] N4 [label="versicolor (48.0/1.0)" shape=box style=filled ] N3->N5 [label="> 4.9"] N5 [label="Petal.Width" ] N5->N6 [label="<= 1.5"] N6 [label="virginica (3.0)" shape=box style=filled ] N5->N7 [label="> 1.5"] N7 [label="versicolor (3.0/1.0)" shape=box style=filled ] N2->N8 [label="> 1.7"] N8 [label="virginica (46.0/1.0)" shape=box style=filled ] } > ## or Rgraphviz > ## Not run: > ##D library("Rgraphviz") > ##D ff <- tempfile() > ##D write_to_dot(m1, ff) > ##D plot(agread(ff)) > ## End(Not run) > ## The first uses the Weka digraph for the classifier tree > di <- rJava::.jcall(m1$classifier, "S", "graph") > writeLines(di) digraph J48Tree { N0 [label="Petal.Width" ] N0->N1 [label="<= 0.6"] N1 [label="setosa (50.0)" shape=box style=filled ] N0->N2 [label="> 0.6"] N2 [label="Petal.Width" ] N2->N3 [label="<= 1.7"] N3 [label="Petal.Length" ] N3->N4 [label="<= 4.9"] N4 [label="versicolor (48.0/1.0)" shape=box style=filled ] N3->N5 [label="> 4.9"] N5 [label="Petal.Width" ] N5->N6 [label="<= 1.5"] N6 [label="virginica (3.0)" shape=box style=filled ] N5->N7 [label="> 1.5"] N7 [label="versicolor (3.0/1.0)" shape=box style=filled ] N2->N8 [label="> 1.7"] N8 [label="virginica (46.0/1.0)" shape=box style=filled ] } > ## for which we can extract the information about nodes and edges via > parse_Weka_digraph(di) $nodes       name splitvar  [1,] "N0" "Petal.Width"  [2,] "N1" ""  [3,] "N2" "Petal.Width"  [4,] "N3" "Petal.Length"  [5,] "N4" ""  [6,] "N5" "Petal.Width"  [7,] "N6" ""  [8,] "N7" ""  [9,] "N8" "" $edges      from to label [1,] "N0" "N1" "<= 0.6" [2,] "N0" "N2" "> 0.6" [3,] "N2" "N3" "<= 1.7" [4,] "N3" "N4" "<= 4.9" [5,] "N3" "N5" "> 4.9" [6,] "N5" "N6" "<= 1.5" [7,] "N5" "N7" "> 1.5" [8,] "N2" "N8" "> 1.7" > > ## Using some Weka data sets ... > > ## J48 > DF2 <- read.arff(system.file("arff", "contact-lenses.arff", + package = "RWeka")) > m2 <- J48(`contact-lenses` ~ ., data = DF2) > m2 J48 pruned tree ------------------ tear-prod-rate = reduced: none (12.0) tear-prod-rate = normal | astigmatism = no: soft (6.0/1.0) | astigmatism = yes | | spectacle-prescrip = myope: hard (3.0) | | spectacle-prescrip = hypermetrope: none (3.0/1.0) Number of Leaves : 4 Size of the tree : 7 > table(DF2$`contact-lenses`, predict(m2))               soft hard none   soft 5 0 0   hard 0 3 1   none 1 0 14 > if(require("partykit", quietly = TRUE)) plot(m2) > > ## M5P > DF3 <- read.arff(system.file("arff", "cpu.arff", package = "RWeka")) > m3 <- M5P(class ~ ., data = DF3) > m3 M5 pruned model tree: (using smoothed linear models) CHMIN <= 7.5 : | MMAX <= 6100 : LM1 (76/7.046%) | MMAX > 6100 : | | CACH <= 28 : | | | CACH <= 0.5 : LM2 (20/7.795%) | | | CACH > 0.5 : | | | | CHMIN <= 3.5 : LM3 (34/11.658%) | | | | CHMIN > 3.5 : LM4 (9/12.866%) | | CACH > 28 : LM5 (26/19.269%) CHMIN > 7.5 : | MMAX <= 28000 : | | MMAX <= 13240 : | | | CACH <= 81.5 : LM6 (6/18.551%) | | | CACH > 81.5 : LM7 (4/30.824%) | | MMAX > 13240 : LM8 (11/24.185%) | MMAX > 28000 : LM9 (23/48.302%) LM num: 1 class =  0.0025 * MMIN  + 0.004 * MMAX  + 0.0009 * CACH  + 0.1758 * CHMIN  + 0.205 * CHMAX  + 7.6605 LM num: 2 class =  -0.012 * MYCT  - 0.0141 * MMIN  + 0.0013 * MMAX  + 0.0017 * CACH  + 0.2793 * CHMIN  + 0.6771 * CHMAX  + 26.4438 LM num: 3 class =  -0.0096 * MMIN  + 0.0028 * MMAX  + 0.0018 * CACH  + 0.2793 * CHMIN  + 0.4086 * CHMAX  + 24.6136 LM num: 4 class =  -0.0406 * MYCT  - 0.0096 * MMIN  + 0.0033 * MMAX  - 0.294 * CACH  + 0.2793 * CHMIN  + 0.4086 * CHMAX  + 38.0329 LM num: 5 class =  -0.0077 * MMIN  + 0.0024 * MMAX  + 0.3125 * CACH  + 0.3866 * CHMIN  + 0.5207 * CHMAX  + 17.2559 LM num: 6 class =  -0.5146 * MYCT  - 0.516 * MMIN  + 0.0086 * MMAX  + 0.3906 * CACH  - 2.0512 * CHMIN  + 70.8672 LM num: 7 class =  -0.5896 * MYCT  - 0.516 * MMIN  + 0.0086 * MMAX  + 0.4034 * CACH  - 2.0512 * CHMIN  + 83.0016 LM num: 8 class =  -0.3653 * MYCT  - 0.516 * MMIN  + 0.0086 * MMAX  + 0.2587 * CACH  - 1.957 * CHMIN  + 82.5725 LM num: 9 class =  -0.4748 * MMIN  + 0.0083 * MMAX  + 0.003 * CACH  - 0.9387 * CHMIN  + 2.2489 * CHMAX  - 51.8474 Number of Rules : 9 > if(require("partykit", quietly = TRUE)) plot(m3) > > ## Logistic Model Tree. > DF4 <- read.arff(system.file("arff", "weather.arff", package = "RWeka")) > m4 <- LMT(play ~ ., data = DF4) > m4 Logistic model tree ------------------ : LM_1:11/11 (14) Number of Leaves : 1 Size of the Tree : 1 LM_1: Class yes : 6.95 + [outlook=sunny] * -0.65 + [outlook=overcast] * 2.82 + [temperature] * -0.02 + [humidity] * -0.06 + [windy=TRUE] * -1.38 Class no : -6.95 + [outlook=sunny] * 0.65 + [outlook=overcast] * -2.82 + [temperature] * 0.02 + [humidity] * 0.06 + [windy=TRUE] * 1.38 > table(DF4$play, predict(m4))             yes no   yes 8 1   no 1 4 > > ## Larger scale example. > if(require("mlbench", quietly = TRUE) + && require("partykit", quietly = TRUE)) { + ## Predict diabetes status for Pima Indian women + data("PimaIndiansDiabetes", package = "mlbench") + ## Fit J48 tree with reduced error pruning + m5 <- J48(diabetes ~ ., data = PimaIndiansDiabetes, + control = Weka_control(R = TRUE)) + plot(m5) + ## (Make sure that the plotting device is big enough for the tree.) + } Warning in data("PimaIndiansDiabetes", package = "mlbench") :   data set ‘PimaIndiansDiabetes’ not found Error in eval(mf, parent.frame()) :   object 'PimaIndiansDiabetes' not found Calls: J48 ... <Anonymous> -> model.frame.default -> is.data.frame Execution halted
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... OK   Running ‘data_exchange.R’   Comparing ‘data_exchange.Rout’ to ‘data_exchange.Rout.save’ ... OK
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [11s/11s] OK
• checking PDF version of manual ... OK
• checking HTML version of manual ... OK
• checking for non-standard things in the check directory ... OK
• checking for detritus in the temp directory ... OK
• DONE

Status: 1 ERROR