Last updated on 2026-07-25 09:50:20 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.4-49 | 7.80 | 89.31 | 97.11 | OK | |
| r-devel-linux-x86_64-debian-gcc | 0.4-49 | 6.78 | 66.59 | 73.37 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.4-49 | 13.00 | 137.51 | 150.51 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 0.4-49 | 71.44 | ERROR | |||
| r-devel-windows-x86_64 | 0.4-49 | 8.00 | 117.00 | 125.00 | OK | |
| r-patched-linux-x86_64 | 0.4-49 | 9.22 | 86.12 | 95.34 | OK | |
| r-release-linux-x86_64 | 0.4-49 | 7.71 | 85.72 | 93.43 | OK | |
| r-release-macos-arm64 | 0.4-49 | 3.00 | 27.00 | 30.00 | OK | |
| r-release-macos-x86_64 | 0.4-49 | 8.00 | 149.00 | 157.00 | OK | |
| r-release-windows-x86_64 | 0.4-49 | 10.00 | 117.00 | 127.00 | OK | |
| r-oldrel-macos-arm64 | 0.4-49 | 2.00 | 31.00 | 33.00 | OK | |
| r-oldrel-macos-x86_64 | 0.4-49 | 8.00 | 121.00 | 129.00 | OK | |
| r-oldrel-windows-x86_64 | 0.4-49 | 9.00 | 126.00 | 135.00 | OK |
Version: 0.4-49
Check: examples
Result: ERROR
Running examples in ‘RWeka-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.4-49
Check: examples
Result: 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
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc