If you think that there is an error in how your package is being tested or represented, please file an issue at NewPkgEval.jl , making sure to read the FAQ first.
Results with Julia v1.2.0
Testing was successful .
Last evaluation was ago and took 1 minute, 58 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed ScikitLearnBase ─ v0.5.0
Installed DecisionTree ──── v0.9.1
Updating `~/.julia/environments/v1.2/Project.toml`
[7806a523] + DecisionTree v0.9.1
Updating `~/.julia/environments/v1.2/Manifest.toml`
[7806a523] + DecisionTree v0.9.1
[6e75b9c4] + ScikitLearnBase v0.5.0
[2a0f44e3] + Base64
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[b77e0a4c] + InteractiveUtils
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[9a3f8284] + Random
[9e88b42a] + Serialization
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[8dfed614] + Test
Testing DecisionTree
Status `/tmp/jl_ccqvk9/Manifest.toml`
[7806a523] DecisionTree v0.9.1
[6e75b9c4] ScikitLearnBase v0.5.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[9a3f8284] Random [`@stdlib/Random`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[8dfed614] Test [`@stdlib/Test`]
Julia version: 1.2.0
TEST: classification/random.jl
Feature 1, Threshold 0.43238112189724165
L-> Feature 5, Threshold 0.48845173204189063
L-> Feature 3, Threshold 0.4016309185953214
L-> 0 : 79/90
R-> -1 : 121/141
R-> Feature 3, Threshold 0.6491270596195745
L-> -1 : 124/131
R-> -1 : 49/69
R-> Feature 3, Threshold 0.3225911070133347
L-> Feature 5, Threshold 0.3214949325746119
L-> 0 : 48/76
R-> 0 : 116/121
R-> Feature 5, Threshold 0.47684569375938257
L-> 0 : 150/171
R-> -1 : 140/201
##### nfoldCV Classification Tree #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 151 19 0
0 29 122 0
0 0 7 0
Accuracy: 0.8198198198198198
Kappa: 0.6500324044069992
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 148 6 0
0 29 131 0
0 0 8 0
Accuracy: 0.8378378378378378
Kappa: 0.6938556616783288
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 135 10 0
0 30 139 0
0 0 15 0
Accuracy: 0.8228228228228228
Kappa: 0.6651155655553282
Mean Accuracy: 0.8268268268268267
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 151 19 0
0 29 122 0
0 0 7 0
Accuracy: 0.8198198198198198
Kappa: 0.6500324044069992
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 148 6 0
0 29 131 0
0 0 8 0
Accuracy: 0.8378378378378378
Kappa: 0.6938556616783288
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 135 10 0
0 30 139 0
0 0 15 0
Accuracy: 0.8228228228228228
Kappa: 0.6651155655553282
Mean Accuracy: 0.8268268268268267
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 142 11 0
0 34 129 0
0 0 12 0
Accuracy: 0.8138138138138138
Kappa: 0.6465936323176994
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 6 0 0
0 141 12 0
0 27 135 0
0 0 12 0
Accuracy: 0.8288288288288288
Kappa: 0.6755883710198429
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 9 0 0
0 151 12 0
0 27 128 0
0 0 6 0
Accuracy: 0.8378378378378378
Kappa: 0.6887742739451002
Mean Accuracy: 0.8268268268268267
##### nfoldCV Classification Forest #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 1 0 0
0 169 1 0
0 0 151 0
0 0 0 7
Accuracy: 0.993993993993994
Kappa: 0.9887057387057387
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
10 1 0 0
1 151 2 0
0 2 158 0
0 0 0 8
Accuracy: 0.9819819819819819
Kappa: 0.9674529223952564
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 0 0 0
0 145 0 0
0 1 168 0
0 0 2 13
Accuracy: 0.990990990990991
Kappa: 0.9835631313962291
Mean Accuracy: 0.988988988988989
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 1 0 0
0 169 1 0
0 0 151 0
0 0 0 7
Accuracy: 0.993993993993994
Kappa: 0.9887057387057387
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
10 1 0 0
1 151 2 0
0 2 158 0
0 0 0 8
Accuracy: 0.9819819819819819
Kappa: 0.9674529223952564
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 0 0 0
0 145 0 0
0 1 168 0
0 0 2 13
Accuracy: 0.990990990990991
Kappa: 0.9835631313962291
Mean Accuracy: 0.988988988988989
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
5 0 0 0
1 151 1 0
0 1 162 0
0 0 4 8
Accuracy: 0.978978978978979
Kappa: 0.9613343064724812
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
6 0 0 0
0 152 1 0
0 1 161 0
0 0 2 10
Accuracy: 0.987987987987988
Kappa: 0.9780762393837646
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
9 0 0 0
0 161 2 0
0 1 154 0
0 0 1 5
Accuracy: 0.987987987987988
Kappa: 0.9778136815630362
Mean Accuracy: 0.984984984984985
##### nfoldCV Adaboosted Stumps #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 147 23 0
0 16 135 0
0 0 7 0
Accuracy: 0.8468468468468469
Kappa: 0.7042010659421047
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 149 5 0
0 26 131 3
0 0 8 0
Accuracy: 0.8408408408408409
Kappa: 0.7017792872712527
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 129 16 0
0 12 157 0
0 0 15 0
Accuracy: 0.8588588588588588
Kappa: 0.7305825242718446
Mean Accuracy: 0.8488488488488488
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 147 23 0
0 16 135 0
0 0 7 0
Accuracy: 0.8468468468468469
Kappa: 0.7042010659421047
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 149 5 0
0 26 131 3
0 0 8 0
Accuracy: 0.8408408408408409
Kappa: 0.7017792872712527
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 129 16 0
0 12 157 0
0 0 15 0
Accuracy: 0.8588588588588588
Kappa: 0.7305825242718446
Mean Accuracy: 0.8488488488488488
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 139 14 0
0 18 145 0
0 0 12 0
Accuracy: 0.8528528528528528
Kappa: 0.7197836166924265
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 6 0 0
0 145 8 0
0 25 137 0
0 0 12 0
Accuracy: 0.8468468468468469
Kappa: 0.709826234045825
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 9 0 0
0 156 7 0
0 20 135 0
0 0 6 0
Accuracy: 0.8738738738738738
Kappa: 0.7580025608194622
Mean Accuracy: 0.8578578578578578
==================================================
TEST: classification/low_precision.jl
##### nfoldCV Classification Tree #####
Fold 1
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
3 0 0 0 0 0 0 0 0
0 12 0 0 0 0 0 0 0
0 0 36 0 0 0 0 0 0
0 0 0 79 0 0 0 0 0
0 0 0 0 98 0 0 0 0
0 0 0 0 0 70 0 0 0
0 0 0 0 0 0 23 0 0
0 0 0 0 0 0 0 10 0
0 0 0 0 0 0 0 0 2
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
2 0 0 0 0 0 0 0 0 0
0 2 0 0 0 0 0 0 0 0
0 0 9 0 0 0 0 0 0 0
0 0 0 33 0 0 0 0 0 0
0 0 0 0 70 0 0 0 0 0
0 0 0 0 0 95 0 0 0 0
0 0 0 0 0 0 70 0 0 0
0 0 0 0 0 0 0 39 0 0
0 0 0 0 0 0 0 0 12 0
0 0 0 0 0 0 0 0 0 1
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
4 0 0 0 0 0 0 0 0
0 11 0 0 0 0 0 0 0
0 0 37 0 0 0 0 0 0
0 0 0 80 0 0 0 0 0
0 0 0 0 87 0 0 0 0
0 0 0 0 0 69 0 0 0
0 0 0 0 0 0 37 0 0
0 0 0 0 0 0 0 6 0
0 0 0 0 0 0 0 0 2
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 1.0
##### nfoldCV Classification Forest #####
Fold 1
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 0 1 0 0 0 0 0 0 0
0 1 1 0 0 0 0 0 0 0
0 0 7 5 0 0 0 0 0 0
0 0 0 32 6 1 0 0 0 0
0 0 0 0 70 6 0 0 0 0
0 0 0 0 0 83 2 0 0 0
0 0 0 0 1 6 64 2 0 0
0 0 0 0 0 0 2 34 0 0
0 0 0 0 0 0 0 5 3 0
0 0 0 0 0 0 0 0 1 0
Accuracy: 0.8828828828828829
Kappa: 0.8538355917705848
Fold 2
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 1 0 0 0 0 0 0 0 0
0 2 1 0 0 0 0 0 0 0
0 1 10 0 0 0 0 0 0 0
0 0 1 24 1 1 0 0 0 0
0 0 0 1 70 4 0 0 0 0
0 0 0 0 2 96 1 0 0 0
0 0 0 0 0 6 65 2 0 0
0 0 0 0 0 0 1 32 0 0
0 0 0 0 0 0 0 2 7 0
0 0 0 0 0 0 0 0 2 0
Accuracy: 0.918918918918919
Kappa: 0.8976457730925982
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
1 2 0 1 0 0 0 0 0
0 7 2 0 0 0 0 0 0
0 0 28 12 0 0 0 0 0
0 0 0 74 4 0 0 0 0
0 0 0 1 93 1 0 0 0
0 0 0 0 6 55 2 0 0
0 0 0 0 1 5 24 0 0
0 0 0 0 0 0 3 9 0
0 0 0 0 0 0 1 1 0
Accuracy: 0.8738738738738738
Kappa: 0.8410627635033012
Mean Accuracy: 0.8918918918918918
##### nfoldCV Adaboosted Stumps #####
Fold 1
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 0 0 0 2 0 0 0 0 0
0 0 0 0 3 0 0 0 0 0
0 0 0 0 10 4 0 0 0 0
0 2 0 0 14 30 0 0 0 0
0 1 0 0 6 67 0 0 0 0
0 5 0 0 0 84 0 0 0 0
0 1 0 0 0 66 0 1 0 0
0 0 0 0 0 17 0 9 0 0
0 0 0 0 0 2 0 8 0 0
0 0 0 0 0 1 0 0 0 0
Accuracy: 0.2972972972972973
Kappa: 0.0688653880623768
Fold 2
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
0 0 0 3 0 0 0 0 0
0 0 0 11 0 0 0 0 0
0 0 8 19 3 1 0 0 0
0 0 16 25 29 9 0 0 0
0 0 1 6 43 48 0 0 0
0 0 0 0 39 30 0 0 0
0 0 0 0 12 21 0 0 0
0 0 0 0 2 6 0 0 0
0 0 0 0 0 1 0 0 0
Accuracy: 0.3183183183183183
Kappa: 0.1055378061767838
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
0 0 0 3 0 0 0 0 0
0 0 0 7 0 0 0 0 0
0 0 0 17 9 3 0 0 0
0 0 0 8 63 4 0 0 0
0 0 0 1 80 12 0 0 0
0 0 0 0 61 11 0 0 0
0 0 0 0 23 10 7 0 0
0 0 0 0 4 1 6 0 0
0 0 0 0 0 0 3 0 0
Accuracy: 0.3183183183183183
Kappa: 0.08126207810202121
Mean Accuracy: 0.3113113113113113
==================================================
TEST: classification/heterogeneous.jl
==================================================
TEST: classification/digits.jl
==================================================
TEST: classification/iris.jl
Feature 3, Threshold 2.45
L-> Iris-setosa : 50/50
R-> Feature 4, Threshold 1.75
L-> Feature 3, Threshold 4.95
L-> Feature 4, Threshold 1.65
L-> Iris-versicolor : 47/47
R-> Iris-virginica : 1/1
R-> Feature 4, Threshold 1.55
L-> Iris-virginica : 3/3
R-> Feature 1, Threshold 6.95
L-> Iris-versicolor : 2/2
R-> Iris-virginica : 1/1
R-> Feature 3, Threshold 4.85
L-> Feature 1, Threshold 5.95
L-> Iris-versicolor : 1/1
R-> Iris-virginica : 2/2
R-> Iris-virginica : 43/43
##### nfoldCV Classification Tree #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
14 0 0
0 19 0
0 0 17
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
18 0 0
0 15 0
0 0 17
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
18 0 0
0 16 0
0 0 16
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 1.0
##### nfoldCV Classification Forest #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
16 0 0
0 18 1
0 0 15
Accuracy: 0.98
Kappa: 0.9699157641395908
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
18 0 0
0 16 0
0 0 16
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
16 0 0
0 14 1
0 1 18
Accuracy: 0.96
Kappa: 0.9396863691194209
Mean Accuracy: 0.98
##### nfoldCV Classification Adaboosted Stumps #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
14 0 0
0 17 0
0 2 17
Accuracy: 0.96
Kappa: 0.9396863691194209
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
19 0 0
0 16 2
0 2 11
Accuracy: 0.92
Kappa: 0.8784933171324424
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
17 0 0
0 14 1
0 2 16
Accuracy: 0.94
Kappa: 0.9099099099099098
Mean Accuracy: 0.94
==================================================
TEST: classification/adult.jl
==================================================
TEST: classification/scikitlearn.jl
==================================================
TEST: regression/random.jl
##### nfoldCV Classification Tree #####
Fold 1
Mean Squared Error: 4.67352967811284
Correlation Coeff: 0.8147290401798616
Coeff of Determination: 0.6623172680793121
Fold 2
Mean Squared Error: 4.342179891353847
Correlation Coeff: 0.8373910448239521
Coeff of Determination: 0.7003052798355491
Fold 3
Mean Squared Error: 4.003285553163639
Correlation Coeff: 0.8270058046714887
Coeff of Determination: 0.6817681645607626
Mean Coeff of Determination: 0.6814635708252079
Fold 1
Mean Squared Error: 4.67352967811284
Correlation Coeff: 0.8147290401798616
Coeff of Determination: 0.6623172680793121
Fold 2
Mean Squared Error: 4.342179891353847
Correlation Coeff: 0.8373910448239521
Coeff of Determination: 0.7003052798355491
Fold 3
Mean Squared Error: 4.003285553163639
Correlation Coeff: 0.8270058046714887
Coeff of Determination: 0.6817681645607626
Mean Coeff of Determination: 0.6814635708252079
Fold 1
Mean Squared Error: 4.074335497973641
Correlation Coeff: 0.8391963844797984
Coeff of Determination: 0.7037000555223341
Fold 2
Mean Squared Error: 4.402381737207544
Correlation Coeff: 0.8207869300719152
Coeff of Determination: 0.6701005379155469
Fold 3
Mean Squared Error: 4.555284426323749
Correlation Coeff: 0.8212077773023579
Coeff of Determination: 0.672545376126066
Mean Coeff of Determination: 0.6821153231879823
##### nfoldCV Regression Forest #####
Fold 1
Mean Squared Error: 1.0898592531967288
Correlation Coeff: 0.968989128137689
Coeff of Determination: 0.9212529553942791
Fold 2
Mean Squared Error: 0.9631419380263889
Correlation Coeff: 0.9747145338520044
Coeff of Determination: 0.93352450593532
Fold 3
Mean Squared Error: 1.0533384060967153
Correlation Coeff: 0.9697636723819906
Coeff of Determination: 0.9162673234623749
Mean Coeff of Determination: 0.9236815949306579
Fold 1
Mean Squared Error: 1.0898592531967288
Correlation Coeff: 0.968989128137689
Coeff of Determination: 0.9212529553942791
Fold 2
Mean Squared Error: 0.9631419380263889
Correlation Coeff: 0.9747145338520044
Coeff of Determination: 0.93352450593532
Fold 3
Mean Squared Error: 1.0533384060967153
Correlation Coeff: 0.9697636723819906
Coeff of Determination: 0.9162673234623749
Mean Coeff of Determination: 0.9236815949306579
Fold 1
Mean Squared Error: 0.9378692828067325
Correlation Coeff: 0.9721097186364827
Coeff of Determination: 0.9317948616256194
Fold 2
Mean Squared Error: 0.9500757797209668
Correlation Coeff: 0.9705235128520393
Coeff of Determination: 0.9288045636705224
Fold 3
Mean Squared Error: 1.107949315333958
Correlation Coeff: 0.973754142148885
Coeff of Determination: 0.9203555492105996
Mean Coeff of Determination: 0.9269849915022471
==================================================
TEST: regression/low_precision.jl
##### nfoldCV Regression Tree #####
Fold 1
Mean Squared Error: 0.9002446380717434
Correlation Coeff: 0.9671265264579381
Coeff of Determination: 0.935022213413093
Fold 2
Mean Squared Error: 0.8600949629501164
Correlation Coeff: 0.9675026105307813
Coeff of Determination: 0.935337447188353
Fold 3
Mean Squared Error: 0.7427796321330984
Correlation Coeff: 0.9727679622659688
Coeff of Determination: 0.9461875493861901
Mean Coeff of Determination: 0.9388490699958787
##### nfoldCV Regression Forest #####
Fold 1
Mean Squared Error: 1.2274348325856186
Correlation Coeff: 0.9704260879283352
Coeff of Determination: 0.9140860432926056
Fold 2
Mean Squared Error: 1.010177816120529
Correlation Coeff: 0.9709054128303982
Coeff of Determination: 0.9278989317966039
Fold 3
Mean Squared Error: 1.01890639749946
Correlation Coeff: 0.9658631947543498
Coeff of Determination: 0.9193339316722111
Mean Coeff of Determination: 0.9204396355871403
==================================================
TEST: regression/digits.jl
==================================================
TEST: regression/scikitlearn.jl
==================================================
Test Summary: | Pass Total
Test Suites | 135 135
Testing DecisionTree tests passed
Results with Julia v1.3.0
Testing was successful .
Last evaluation was ago and took 2 minutes, 15 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed ScikitLearnBase ─ v0.5.0
Installed DecisionTree ──── v0.9.1
Updating `~/.julia/environments/v1.3/Project.toml`
[7806a523] + DecisionTree v0.9.1
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7806a523] + DecisionTree v0.9.1
[6e75b9c4] + ScikitLearnBase v0.5.0
[2a0f44e3] + Base64
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[b77e0a4c] + InteractiveUtils
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[9a3f8284] + Random
[9e88b42a] + Serialization
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[8dfed614] + Test
Testing DecisionTree
Status `/tmp/jl_pVtUdk/Manifest.toml`
[7806a523] DecisionTree v0.9.1
[6e75b9c4] ScikitLearnBase v0.5.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[9a3f8284] Random [`@stdlib/Random`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[8dfed614] Test [`@stdlib/Test`]
Julia version: 1.3.0
TEST: classification/random.jl
Feature 1, Threshold 0.43238112189724165
L-> Feature 5, Threshold 0.48845173204189063
L-> Feature 3, Threshold 0.4016309185953214
L-> 0 : 79/90
R-> -1 : 121/141
R-> Feature 3, Threshold 0.6491270596195745
L-> -1 : 124/131
R-> -1 : 49/69
R-> Feature 3, Threshold 0.3225911070133347
L-> Feature 5, Threshold 0.3214949325746119
L-> 0 : 48/76
R-> 0 : 116/121
R-> Feature 5, Threshold 0.47684569375938257
L-> 0 : 150/171
R-> -1 : 140/201
##### nfoldCV Classification Tree #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 151 19 0
0 29 122 0
0 0 7 0
Accuracy: 0.8198198198198198
Kappa: 0.6500324044069992
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 148 6 0
0 29 131 0
0 0 8 0
Accuracy: 0.8378378378378378
Kappa: 0.6938556616783288
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 135 10 0
0 30 139 0
0 0 15 0
Accuracy: 0.8228228228228228
Kappa: 0.6651155655553282
Mean Accuracy: 0.8268268268268267
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 151 19 0
0 29 122 0
0 0 7 0
Accuracy: 0.8198198198198198
Kappa: 0.6500324044069992
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 148 6 0
0 29 131 0
0 0 8 0
Accuracy: 0.8378378378378378
Kappa: 0.6938556616783288
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 135 10 0
0 30 139 0
0 0 15 0
Accuracy: 0.8228228228228228
Kappa: 0.6651155655553282
Mean Accuracy: 0.8268268268268267
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 142 11 0
0 34 129 0
0 0 12 0
Accuracy: 0.8138138138138138
Kappa: 0.6465936323176994
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 6 0 0
0 141 12 0
0 27 135 0
0 0 12 0
Accuracy: 0.8288288288288288
Kappa: 0.6755883710198429
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 9 0 0
0 151 12 0
0 27 128 0
0 0 6 0
Accuracy: 0.8378378378378378
Kappa: 0.6887742739451002
Mean Accuracy: 0.8268268268268267
##### nfoldCV Classification Forest #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 1 0 0
0 169 1 0
0 0 151 0
0 0 0 7
Accuracy: 0.993993993993994
Kappa: 0.9887057387057387
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
10 1 0 0
1 151 2 0
0 2 158 0
0 0 0 8
Accuracy: 0.9819819819819819
Kappa: 0.9674529223952564
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 0 0 0
0 145 0 0
0 1 168 0
0 0 2 13
Accuracy: 0.990990990990991
Kappa: 0.9835631313962291
Mean Accuracy: 0.988988988988989
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 1 0 0
0 169 1 0
0 0 151 0
0 0 0 7
Accuracy: 0.993993993993994
Kappa: 0.9887057387057387
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
10 1 0 0
1 151 2 0
0 2 158 0
0 0 0 8
Accuracy: 0.9819819819819819
Kappa: 0.9674529223952564
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 0 0 0
0 145 0 0
0 1 168 0
0 0 2 13
Accuracy: 0.990990990990991
Kappa: 0.9835631313962291
Mean Accuracy: 0.988988988988989
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
5 0 0 0
1 151 1 0
0 1 162 0
0 0 4 8
Accuracy: 0.978978978978979
Kappa: 0.9613343064724812
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
6 0 0 0
0 152 1 0
0 1 161 0
0 0 2 10
Accuracy: 0.987987987987988
Kappa: 0.9780762393837646
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
9 0 0 0
0 161 2 0
0 1 154 0
0 0 1 5
Accuracy: 0.987987987987988
Kappa: 0.9778136815630362
Mean Accuracy: 0.984984984984985
##### nfoldCV Adaboosted Stumps #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 147 23 0
0 16 135 0
0 0 7 0
Accuracy: 0.8468468468468469
Kappa: 0.7042010659421047
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 149 5 0
0 26 131 3
0 0 8 0
Accuracy: 0.8408408408408409
Kappa: 0.7017792872712527
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 129 16 0
0 12 157 0
0 0 15 0
Accuracy: 0.8588588588588588
Kappa: 0.7305825242718446
Mean Accuracy: 0.8488488488488488
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 147 23 0
0 16 135 0
0 0 7 0
Accuracy: 0.8468468468468469
Kappa: 0.7042010659421047
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 149 5 0
0 26 131 3
0 0 8 0
Accuracy: 0.8408408408408409
Kappa: 0.7017792872712527
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 129 16 0
0 12 157 0
0 0 15 0
Accuracy: 0.8588588588588588
Kappa: 0.7305825242718446
Mean Accuracy: 0.8488488488488488
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 139 14 0
0 18 145 0
0 0 12 0
Accuracy: 0.8528528528528528
Kappa: 0.7197836166924265
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 6 0 0
0 145 8 0
0 25 137 0
0 0 12 0
Accuracy: 0.8468468468468469
Kappa: 0.709826234045825
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 9 0 0
0 156 7 0
0 20 135 0
0 0 6 0
Accuracy: 0.8738738738738738
Kappa: 0.7580025608194622
Mean Accuracy: 0.8578578578578578
==================================================
TEST: classification/low_precision.jl
##### nfoldCV Classification Tree #####
Fold 1
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
3 0 0 0 0 0 0 0 0
0 12 0 0 0 0 0 0 0
0 0 36 0 0 0 0 0 0
0 0 0 79 0 0 0 0 0
0 0 0 0 98 0 0 0 0
0 0 0 0 0 70 0 0 0
0 0 0 0 0 0 23 0 0
0 0 0 0 0 0 0 10 0
0 0 0 0 0 0 0 0 2
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
2 0 0 0 0 0 0 0 0 0
0 2 0 0 0 0 0 0 0 0
0 0 9 0 0 0 0 0 0 0
0 0 0 33 0 0 0 0 0 0
0 0 0 0 70 0 0 0 0 0
0 0 0 0 0 95 0 0 0 0
0 0 0 0 0 0 70 0 0 0
0 0 0 0 0 0 0 39 0 0
0 0 0 0 0 0 0 0 12 0
0 0 0 0 0 0 0 0 0 1
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
4 0 0 0 0 0 0 0 0
0 11 0 0 0 0 0 0 0
0 0 37 0 0 0 0 0 0
0 0 0 80 0 0 0 0 0
0 0 0 0 87 0 0 0 0
0 0 0 0 0 69 0 0 0
0 0 0 0 0 0 37 0 0
0 0 0 0 0 0 0 6 0
0 0 0 0 0 0 0 0 2
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 1.0
##### nfoldCV Classification Forest #####
Fold 1
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 0 1 0 0 0 0 0 0 0
0 1 1 0 0 0 0 0 0 0
0 0 7 5 0 0 0 0 0 0
0 0 0 32 6 1 0 0 0 0
0 0 0 0 70 6 0 0 0 0
0 0 0 0 0 83 2 0 0 0
0 0 0 0 1 6 64 2 0 0
0 0 0 0 0 0 2 34 0 0
0 0 0 0 0 0 0 5 3 0
0 0 0 0 0 0 0 0 1 0
Accuracy: 0.8828828828828829
Kappa: 0.8538355917705848
Fold 2
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 1 0 0 0 0 0 0 0 0
0 2 1 0 0 0 0 0 0 0
0 1 10 0 0 0 0 0 0 0
0 0 1 24 1 1 0 0 0 0
0 0 0 1 70 4 0 0 0 0
0 0 0 0 2 96 1 0 0 0
0 0 0 0 0 6 65 2 0 0
0 0 0 0 0 0 1 32 0 0
0 0 0 0 0 0 0 2 7 0
0 0 0 0 0 0 0 0 2 0
Accuracy: 0.918918918918919
Kappa: 0.8976457730925982
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
1 2 0 1 0 0 0 0 0
0 7 2 0 0 0 0 0 0
0 0 28 12 0 0 0 0 0
0 0 0 74 4 0 0 0 0
0 0 0 1 93 1 0 0 0
0 0 0 0 6 55 2 0 0
0 0 0 0 1 5 24 0 0
0 0 0 0 0 0 3 9 0
0 0 0 0 0 0 1 1 0
Accuracy: 0.8738738738738738
Kappa: 0.8410627635033012
Mean Accuracy: 0.8918918918918918
##### nfoldCV Adaboosted Stumps #####
Fold 1
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 0 0 0 2 0 0 0 0 0
0 0 0 0 3 0 0 0 0 0
0 0 0 0 10 4 0 0 0 0
0 2 0 0 14 30 0 0 0 0
0 1 0 0 6 67 0 0 0 0
0 5 0 0 0 84 0 0 0 0
0 1 0 0 0 66 0 1 0 0
0 0 0 0 0 17 0 9 0 0
0 0 0 0 0 2 0 8 0 0
0 0 0 0 0 1 0 0 0 0
Accuracy: 0.2972972972972973
Kappa: 0.0688653880623768
Fold 2
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
0 0 0 3 0 0 0 0 0
0 0 0 11 0 0 0 0 0
0 0 8 19 3 1 0 0 0
0 0 16 25 29 9 0 0 0
0 0 1 6 43 48 0 0 0
0 0 0 0 39 30 0 0 0
0 0 0 0 12 21 0 0 0
0 0 0 0 2 6 0 0 0
0 0 0 0 0 1 0 0 0
Accuracy: 0.3183183183183183
Kappa: 0.1055378061767838
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
0 0 0 3 0 0 0 0 0
0 0 0 7 0 0 0 0 0
0 0 0 17 9 3 0 0 0
0 0 0 8 63 4 0 0 0
0 0 0 1 80 12 0 0 0
0 0 0 0 61 11 0 0 0
0 0 0 0 23 10 7 0 0
0 0 0 0 4 1 6 0 0
0 0 0 0 0 0 3 0 0
Accuracy: 0.3183183183183183
Kappa: 0.08126207810202121
Mean Accuracy: 0.3113113113113113
==================================================
TEST: classification/heterogeneous.jl
==================================================
TEST: classification/digits.jl
==================================================
TEST: classification/iris.jl
Feature 4, Threshold 0.8
L-> Iris-setosa : 50/50
R-> Feature 4, Threshold 1.75
L-> Feature 3, Threshold 4.95
L-> Feature 4, Threshold 1.65
L-> Iris-versicolor : 47/47
R-> Iris-virginica : 1/1
R-> Feature 4, Threshold 1.55
L-> Iris-virginica : 3/3
R-> Feature 1, Threshold 6.95
L-> Iris-versicolor : 2/2
R-> Iris-virginica : 1/1
R-> Feature 3, Threshold 4.85
L-> Feature 1, Threshold 5.95
L-> Iris-versicolor : 1/1
R-> Iris-virginica : 2/2
R-> Iris-virginica : 43/43
##### nfoldCV Classification Tree #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
20 0 0
0 13 0
0 0 17
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
14 0 0
0 17 0
0 0 19
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
16 0 0
0 20 0
0 0 14
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 1.0
##### nfoldCV Classification Forest #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
17 0 0
0 22 0
0 1 10
Accuracy: 0.98
Kappa: 0.9686520376175548
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
19 0 0
0 11 0
0 1 19
Accuracy: 0.98
Kappa: 0.9692685925015365
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
14 0 0
0 17 0
0 0 19
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 0.9866666666666667
##### nfoldCV Classification Adaboosted Stumps #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
15 0 0
0 22 0
0 2 11
Accuracy: 0.96
Kappa: 0.9376558603491271
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
21 0 0
0 11 1
0 1 16
Accuracy: 0.96
Kappa: 0.9384993849938499
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
14 0 0
0 15 1
0 4 16
Accuracy: 0.9
Kappa: 0.849397590361446
Mean Accuracy: 0.94
==================================================
TEST: classification/adult.jl
==================================================
TEST: classification/scikitlearn.jl
==================================================
TEST: regression/random.jl
##### nfoldCV Classification Tree #####
Fold 1
Mean Squared Error: 4.67352967811284
Correlation Coeff: 0.8147290401798616
Coeff of Determination: 0.6623172680793121
Fold 2
Mean Squared Error: 4.342179891353847
Correlation Coeff: 0.8373910448239521
Coeff of Determination: 0.7003052798355491
Fold 3
Mean Squared Error: 4.003285553163639
Correlation Coeff: 0.8270058046714887
Coeff of Determination: 0.6817681645607626
Mean Coeff of Determination: 0.6814635708252079
Fold 1
Mean Squared Error: 4.67352967811284
Correlation Coeff: 0.8147290401798616
Coeff of Determination: 0.6623172680793121
Fold 2
Mean Squared Error: 4.342179891353847
Correlation Coeff: 0.8373910448239521
Coeff of Determination: 0.7003052798355491
Fold 3
Mean Squared Error: 4.003285553163639
Correlation Coeff: 0.8270058046714887
Coeff of Determination: 0.6817681645607626
Mean Coeff of Determination: 0.6814635708252079
Fold 1
Mean Squared Error: 4.074335497973641
Correlation Coeff: 0.8391963844797984
Coeff of Determination: 0.7037000555223341
Fold 2
Mean Squared Error: 4.402381737207544
Correlation Coeff: 0.8207869300719152
Coeff of Determination: 0.6701005379155469
Fold 3
Mean Squared Error: 4.555284426323749
Correlation Coeff: 0.8212077773023579
Coeff of Determination: 0.672545376126066
Mean Coeff of Determination: 0.6821153231879823
##### nfoldCV Regression Forest #####
Fold 1
Mean Squared Error: 1.0898592531967288
Correlation Coeff: 0.968989128137689
Coeff of Determination: 0.9212529553942791
Fold 2
Mean Squared Error: 0.9631419380263889
Correlation Coeff: 0.9747145338520044
Coeff of Determination: 0.93352450593532
Fold 3
Mean Squared Error: 1.0533384060967153
Correlation Coeff: 0.9697636723819906
Coeff of Determination: 0.9162673234623749
Mean Coeff of Determination: 0.9236815949306579
Fold 1
Mean Squared Error: 1.0898592531967288
Correlation Coeff: 0.968989128137689
Coeff of Determination: 0.9212529553942791
Fold 2
Mean Squared Error: 0.9631419380263889
Correlation Coeff: 0.9747145338520044
Coeff of Determination: 0.93352450593532
Fold 3
Mean Squared Error: 1.0533384060967153
Correlation Coeff: 0.9697636723819906
Coeff of Determination: 0.9162673234623749
Mean Coeff of Determination: 0.9236815949306579
Fold 1
Mean Squared Error: 0.9378692828067325
Correlation Coeff: 0.9721097186364827
Coeff of Determination: 0.9317948616256194
Fold 2
Mean Squared Error: 0.9500757797209668
Correlation Coeff: 0.9705235128520393
Coeff of Determination: 0.9288045636705224
Fold 3
Mean Squared Error: 1.107949315333958
Correlation Coeff: 0.973754142148885
Coeff of Determination: 0.9203555492105996
Mean Coeff of Determination: 0.9269849915022471
==================================================
TEST: regression/low_precision.jl
##### nfoldCV Regression Tree #####
Fold 1
Mean Squared Error: 0.9002446380717434
Correlation Coeff: 0.9671265264579381
Coeff of Determination: 0.935022213413093
Fold 2
Mean Squared Error: 0.8600949629501164
Correlation Coeff: 0.9675026105307813
Coeff of Determination: 0.935337447188353
Fold 3
Mean Squared Error: 0.7427796321330984
Correlation Coeff: 0.9727679622659688
Coeff of Determination: 0.9461875493861901
Mean Coeff of Determination: 0.9388490699958787
##### nfoldCV Regression Forest #####
Fold 1
Mean Squared Error: 1.2274348325856186
Correlation Coeff: 0.9704260879283352
Coeff of Determination: 0.9140860432926056
Fold 2
Mean Squared Error: 1.010177816120529
Correlation Coeff: 0.9709054128303982
Coeff of Determination: 0.9278989317966039
Fold 3
Mean Squared Error: 1.01890639749946
Correlation Coeff: 0.9658631947543498
Coeff of Determination: 0.9193339316722111
Mean Coeff of Determination: 0.9204396355871403
==================================================
TEST: regression/digits.jl
==================================================
TEST: regression/scikitlearn.jl
==================================================
Test Summary: | Pass Total
Test Suites | 135 135
Testing DecisionTree tests passed
Results with Julia v1.3.1-pre-7704df0a5a
Testing was successful .
Last evaluation was ago and took 1 minute, 57 seconds.
Click here to download the log file.
Click here to show the log contents.
Resolving package versions...
Installed ScikitLearnBase ─ v0.5.0
Installed DecisionTree ──── v0.9.1
Updating `~/.julia/environments/v1.3/Project.toml`
[7806a523] + DecisionTree v0.9.1
Updating `~/.julia/environments/v1.3/Manifest.toml`
[7806a523] + DecisionTree v0.9.1
[6e75b9c4] + ScikitLearnBase v0.5.0
[2a0f44e3] + Base64
[8bb1440f] + DelimitedFiles
[8ba89e20] + Distributed
[b77e0a4c] + InteractiveUtils
[8f399da3] + Libdl
[37e2e46d] + LinearAlgebra
[56ddb016] + Logging
[d6f4376e] + Markdown
[a63ad114] + Mmap
[9a3f8284] + Random
[9e88b42a] + Serialization
[6462fe0b] + Sockets
[2f01184e] + SparseArrays
[10745b16] + Statistics
[8dfed614] + Test
Testing DecisionTree
Status `/tmp/jl_Nonnbs/Manifest.toml`
[7806a523] DecisionTree v0.9.1
[6e75b9c4] ScikitLearnBase v0.5.0
[2a0f44e3] Base64 [`@stdlib/Base64`]
[8bb1440f] DelimitedFiles [`@stdlib/DelimitedFiles`]
[8ba89e20] Distributed [`@stdlib/Distributed`]
[b77e0a4c] InteractiveUtils [`@stdlib/InteractiveUtils`]
[8f399da3] Libdl [`@stdlib/Libdl`]
[37e2e46d] LinearAlgebra [`@stdlib/LinearAlgebra`]
[56ddb016] Logging [`@stdlib/Logging`]
[d6f4376e] Markdown [`@stdlib/Markdown`]
[a63ad114] Mmap [`@stdlib/Mmap`]
[9a3f8284] Random [`@stdlib/Random`]
[9e88b42a] Serialization [`@stdlib/Serialization`]
[6462fe0b] Sockets [`@stdlib/Sockets`]
[2f01184e] SparseArrays [`@stdlib/SparseArrays`]
[10745b16] Statistics [`@stdlib/Statistics`]
[8dfed614] Test [`@stdlib/Test`]
Julia version: 1.3.1-pre.11
TEST: classification/random.jl
Feature 1, Threshold 0.43238112189724165
L-> Feature 5, Threshold 0.48845173204189063
L-> Feature 3, Threshold 0.4016309185953214
L-> 0 : 79/90
R-> -1 : 121/141
R-> Feature 3, Threshold 0.6491270596195745
L-> -1 : 124/131
R-> -1 : 49/69
R-> Feature 3, Threshold 0.3225911070133347
L-> Feature 5, Threshold 0.3214949325746119
L-> 0 : 48/76
R-> 0 : 116/121
R-> Feature 5, Threshold 0.47684569375938257
L-> 0 : 150/171
R-> -1 : 140/201
##### nfoldCV Classification Tree #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 151 19 0
0 29 122 0
0 0 7 0
Accuracy: 0.8198198198198198
Kappa: 0.6500324044069992
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 148 6 0
0 29 131 0
0 0 8 0
Accuracy: 0.8378378378378378
Kappa: 0.6938556616783288
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 135 10 0
0 30 139 0
0 0 15 0
Accuracy: 0.8228228228228228
Kappa: 0.6651155655553282
Mean Accuracy: 0.8268268268268267
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 151 19 0
0 29 122 0
0 0 7 0
Accuracy: 0.8198198198198198
Kappa: 0.6500324044069992
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 148 6 0
0 29 131 0
0 0 8 0
Accuracy: 0.8378378378378378
Kappa: 0.6938556616783288
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 135 10 0
0 30 139 0
0 0 15 0
Accuracy: 0.8228228228228228
Kappa: 0.6651155655553282
Mean Accuracy: 0.8268268268268267
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 142 11 0
0 34 129 0
0 0 12 0
Accuracy: 0.8138138138138138
Kappa: 0.6465936323176994
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 6 0 0
0 141 12 0
0 27 135 0
0 0 12 0
Accuracy: 0.8288288288288288
Kappa: 0.6755883710198429
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 9 0 0
0 151 12 0
0 27 128 0
0 0 6 0
Accuracy: 0.8378378378378378
Kappa: 0.6887742739451002
Mean Accuracy: 0.8268268268268267
##### nfoldCV Classification Forest #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 1 0 0
0 169 1 0
0 0 151 0
0 0 0 7
Accuracy: 0.993993993993994
Kappa: 0.9887057387057387
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
10 1 0 0
1 151 2 0
0 2 158 0
0 0 0 8
Accuracy: 0.9819819819819819
Kappa: 0.9674529223952564
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 0 0 0
0 145 0 0
0 1 168 0
0 0 2 13
Accuracy: 0.990990990990991
Kappa: 0.9835631313962291
Mean Accuracy: 0.988988988988989
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 1 0 0
0 169 1 0
0 0 151 0
0 0 0 7
Accuracy: 0.993993993993994
Kappa: 0.9887057387057387
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
10 1 0 0
1 151 2 0
0 2 158 0
0 0 0 8
Accuracy: 0.9819819819819819
Kappa: 0.9674529223952564
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
4 0 0 0
0 145 0 0
0 1 168 0
0 0 2 13
Accuracy: 0.990990990990991
Kappa: 0.9835631313962291
Mean Accuracy: 0.988988988988989
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
5 0 0 0
1 151 1 0
0 1 162 0
0 0 4 8
Accuracy: 0.978978978978979
Kappa: 0.9613343064724812
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
6 0 0 0
0 152 1 0
0 1 161 0
0 0 2 10
Accuracy: 0.987987987987988
Kappa: 0.9780762393837646
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
9 0 0 0
0 161 2 0
0 1 154 0
0 0 1 5
Accuracy: 0.987987987987988
Kappa: 0.9778136815630362
Mean Accuracy: 0.984984984984985
##### nfoldCV Adaboosted Stumps #####
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 147 23 0
0 16 135 0
0 0 7 0
Accuracy: 0.8468468468468469
Kappa: 0.7042010659421047
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 149 5 0
0 26 131 3
0 0 8 0
Accuracy: 0.8408408408408409
Kappa: 0.7017792872712527
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 129 16 0
0 12 157 0
0 0 15 0
Accuracy: 0.8588588588588588
Kappa: 0.7305825242718446
Mean Accuracy: 0.8488488488488488
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 147 23 0
0 16 135 0
0 0 7 0
Accuracy: 0.8468468468468469
Kappa: 0.7042010659421047
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 11 0 0
0 149 5 0
0 26 131 3
0 0 8 0
Accuracy: 0.8408408408408409
Kappa: 0.7017792872712527
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 4 0 0
0 129 16 0
0 12 157 0
0 0 15 0
Accuracy: 0.8588588588588588
Kappa: 0.7305825242718446
Mean Accuracy: 0.8488488488488488
Fold 1
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 5 0 0
0 139 14 0
0 18 145 0
0 0 12 0
Accuracy: 0.8528528528528528
Kappa: 0.7197836166924265
Fold 2
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 6 0 0
0 145 8 0
0 25 137 0
0 0 12 0
Accuracy: 0.8468468468468469
Kappa: 0.709826234045825
Fold 3
Classes: [-2, -1, 0, 1]
Matrix: 4×4 Array{Int64,2}:
0 9 0 0
0 156 7 0
0 20 135 0
0 0 6 0
Accuracy: 0.8738738738738738
Kappa: 0.7580025608194622
Mean Accuracy: 0.8578578578578578
==================================================
TEST: classification/low_precision.jl
##### nfoldCV Classification Tree #####
Fold 1
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
3 0 0 0 0 0 0 0 0
0 12 0 0 0 0 0 0 0
0 0 36 0 0 0 0 0 0
0 0 0 79 0 0 0 0 0
0 0 0 0 98 0 0 0 0
0 0 0 0 0 70 0 0 0
0 0 0 0 0 0 23 0 0
0 0 0 0 0 0 0 10 0
0 0 0 0 0 0 0 0 2
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
2 0 0 0 0 0 0 0 0 0
0 2 0 0 0 0 0 0 0 0
0 0 9 0 0 0 0 0 0 0
0 0 0 33 0 0 0 0 0 0
0 0 0 0 70 0 0 0 0 0
0 0 0 0 0 95 0 0 0 0
0 0 0 0 0 0 70 0 0 0
0 0 0 0 0 0 0 39 0 0
0 0 0 0 0 0 0 0 12 0
0 0 0 0 0 0 0 0 0 1
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
4 0 0 0 0 0 0 0 0
0 11 0 0 0 0 0 0 0
0 0 37 0 0 0 0 0 0
0 0 0 80 0 0 0 0 0
0 0 0 0 87 0 0 0 0
0 0 0 0 0 69 0 0 0
0 0 0 0 0 0 37 0 0
0 0 0 0 0 0 0 6 0
0 0 0 0 0 0 0 0 2
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 1.0
##### nfoldCV Classification Forest #####
Fold 1
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 0 1 0 0 0 0 0 0 0
0 1 1 0 0 0 0 0 0 0
0 0 7 5 0 0 0 0 0 0
0 0 0 32 6 1 0 0 0 0
0 0 0 0 70 6 0 0 0 0
0 0 0 0 0 83 2 0 0 0
0 0 0 0 1 6 64 2 0 0
0 0 0 0 0 0 2 34 0 0
0 0 0 0 0 0 0 5 3 0
0 0 0 0 0 0 0 0 1 0
Accuracy: 0.8828828828828829
Kappa: 0.8538355917705848
Fold 2
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 1 0 0 0 0 0 0 0 0
0 2 1 0 0 0 0 0 0 0
0 1 10 0 0 0 0 0 0 0
0 0 1 24 1 1 0 0 0 0
0 0 0 1 70 4 0 0 0 0
0 0 0 0 2 96 1 0 0 0
0 0 0 0 0 6 65 2 0 0
0 0 0 0 0 0 1 32 0 0
0 0 0 0 0 0 0 2 7 0
0 0 0 0 0 0 0 0 2 0
Accuracy: 0.918918918918919
Kappa: 0.8976457730925982
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
1 2 0 1 0 0 0 0 0
0 7 2 0 0 0 0 0 0
0 0 28 12 0 0 0 0 0
0 0 0 74 4 0 0 0 0
0 0 0 1 93 1 0 0 0
0 0 0 0 6 55 2 0 0
0 0 0 0 1 5 24 0 0
0 0 0 0 0 0 3 9 0
0 0 0 0 0 0 1 1 0
Accuracy: 0.8738738738738738
Kappa: 0.8410627635033012
Mean Accuracy: 0.8918918918918918
##### nfoldCV Adaboosted Stumps #####
Fold 1
Classes: Int32[-5, -4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 10×10 Array{Int64,2}:
0 0 0 0 2 0 0 0 0 0
0 0 0 0 3 0 0 0 0 0
0 0 0 0 10 4 0 0 0 0
0 2 0 0 14 30 0 0 0 0
0 1 0 0 6 67 0 0 0 0
0 5 0 0 0 84 0 0 0 0
0 1 0 0 0 66 0 1 0 0
0 0 0 0 0 17 0 9 0 0
0 0 0 0 0 2 0 8 0 0
0 0 0 0 0 1 0 0 0 0
Accuracy: 0.2972972972972973
Kappa: 0.0688653880623768
Fold 2
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
0 0 0 3 0 0 0 0 0
0 0 0 11 0 0 0 0 0
0 0 8 19 3 1 0 0 0
0 0 16 25 29 9 0 0 0
0 0 1 6 43 48 0 0 0
0 0 0 0 39 30 0 0 0
0 0 0 0 12 21 0 0 0
0 0 0 0 2 6 0 0 0
0 0 0 0 0 1 0 0 0
Accuracy: 0.3183183183183183
Kappa: 0.1055378061767838
Fold 3
Classes: Int32[-4, -3, -2, -1, 0, 1, 2, 3, 4]
Matrix: 9×9 Array{Int64,2}:
0 0 0 3 0 0 0 0 0
0 0 0 7 0 0 0 0 0
0 0 0 17 9 3 0 0 0
0 0 0 8 63 4 0 0 0
0 0 0 1 80 12 0 0 0
0 0 0 0 61 11 0 0 0
0 0 0 0 23 10 7 0 0
0 0 0 0 4 1 6 0 0
0 0 0 0 0 0 3 0 0
Accuracy: 0.3183183183183183
Kappa: 0.08126207810202121
Mean Accuracy: 0.3113113113113113
==================================================
TEST: classification/heterogeneous.jl
==================================================
TEST: classification/digits.jl
==================================================
TEST: classification/iris.jl
Feature 3, Threshold 2.45
L-> Iris-setosa : 50/50
R-> Feature 4, Threshold 1.75
L-> Feature 3, Threshold 4.95
L-> Feature 4, Threshold 1.65
L-> Iris-versicolor : 47/47
R-> Iris-virginica : 1/1
R-> Feature 4, Threshold 1.55
L-> Iris-virginica : 3/3
R-> Feature 3, Threshold 5.449999999999999
L-> Iris-versicolor : 2/2
R-> Iris-virginica : 1/1
R-> Feature 3, Threshold 4.85
L-> Feature 2, Threshold 3.1
L-> Iris-virginica : 2/2
R-> Iris-versicolor : 1/1
R-> Iris-virginica : 43/43
##### nfoldCV Classification Tree #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
17 0 0
0 18 0
0 0 15
Accuracy: 1.0
Kappa: 1.0
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
19 0 0
0 16 0
0 0 15
Accuracy: 1.0
Kappa: 1.0
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
14 0 0
0 16 0
0 0 20
Accuracy: 1.0
Kappa: 1.0
Mean Accuracy: 1.0
##### nfoldCV Classification Forest #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
10 0 0
0 17 2
0 0 21
Accuracy: 0.96
Kappa: 0.9372647427854454
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
28 0 0
0 14 1
0 0 7
Accuracy: 0.98
Kappa: 0.9655172413793103
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
12 0 0
0 16 0
0 1 21
Accuracy: 0.98
Kappa: 0.969173859432799
Mean Accuracy: 0.9733333333333333
##### nfoldCV Classification Adaboosted Stumps #####
Fold 1
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
20 0 0
0 12 3
0 2 13
Accuracy: 0.9
Kappa: 0.8484848484848486
Fold 2
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
12 0 0
0 18 0
0 3 17
Accuracy: 0.94
Kappa: 0.9084249084249083
Fold 3
Classes: ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
Matrix: 3×3 Array{Int64,2}:
18 0 0
0 16 1
0 2 13
Accuracy: 0.94
Kappa: 0.9096385542168673
Mean Accuracy: 0.9266666666666666
==================================================
TEST: classification/adult.jl
==================================================
TEST: classification/scikitlearn.jl
==================================================
TEST: regression/random.jl
##### nfoldCV Classification Tree #####
Fold 1
Mean Squared Error: 4.67352967811284
Correlation Coeff: 0.8147290401798616
Coeff of Determination: 0.6623172680793121
Fold 2
Mean Squared Error: 4.342179891353847
Correlation Coeff: 0.8373910448239521
Coeff of Determination: 0.7003052798355491
Fold 3
Mean Squared Error: 4.003285553163639
Correlation Coeff: 0.8270058046714887
Coeff of Determination: 0.6817681645607626
Mean Coeff of Determination: 0.6814635708252079
Fold 1
Mean Squared Error: 4.67352967811284
Correlation Coeff: 0.8147290401798616
Coeff of Determination: 0.6623172680793121
Fold 2
Mean Squared Error: 4.342179891353847
Correlation Coeff: 0.8373910448239521
Coeff of Determination: 0.7003052798355491
Fold 3
Mean Squared Error: 4.003285553163639
Correlation Coeff: 0.8270058046714887
Coeff of Determination: 0.6817681645607626
Mean Coeff of Determination: 0.6814635708252079
Fold 1
Mean Squared Error: 4.074335497973641
Correlation Coeff: 0.8391963844797984
Coeff of Determination: 0.7037000555223341
Fold 2
Mean Squared Error: 4.402381737207544
Correlation Coeff: 0.8207869300719152
Coeff of Determination: 0.6701005379155469
Fold 3
Mean Squared Error: 4.555284426323749
Correlation Coeff: 0.8212077773023579
Coeff of Determination: 0.672545376126066
Mean Coeff of Determination: 0.6821153231879823
##### nfoldCV Regression Forest #####
Fold 1
Mean Squared Error: 1.0898592531967288
Correlation Coeff: 0.968989128137689
Coeff of Determination: 0.9212529553942791
Fold 2
Mean Squared Error: 0.9631419380263889
Correlation Coeff: 0.9747145338520044
Coeff of Determination: 0.93352450593532
Fold 3
Mean Squared Error: 1.0533384060967153
Correlation Coeff: 0.9697636723819906
Coeff of Determination: 0.9162673234623749
Mean Coeff of Determination: 0.9236815949306579
Fold 1
Mean Squared Error: 1.0898592531967288
Correlation Coeff: 0.968989128137689
Coeff of Determination: 0.9212529553942791
Fold 2
Mean Squared Error: 0.9631419380263889
Correlation Coeff: 0.9747145338520044
Coeff of Determination: 0.93352450593532
Fold 3
Mean Squared Error: 1.0533384060967153
Correlation Coeff: 0.9697636723819906
Coeff of Determination: 0.9162673234623749
Mean Coeff of Determination: 0.9236815949306579
Fold 1
Mean Squared Error: 0.9378692828067325
Correlation Coeff: 0.9721097186364827
Coeff of Determination: 0.9317948616256194
Fold 2
Mean Squared Error: 0.9500757797209668
Correlation Coeff: 0.9705235128520393
Coeff of Determination: 0.9288045636705224
Fold 3
Mean Squared Error: 1.107949315333958
Correlation Coeff: 0.973754142148885
Coeff of Determination: 0.9203555492105996
Mean Coeff of Determination: 0.9269849915022471
==================================================
TEST: regression/low_precision.jl
##### nfoldCV Regression Tree #####
Fold 1
Mean Squared Error: 0.9002446380717434
Correlation Coeff: 0.9671265264579381
Coeff of Determination: 0.935022213413093
Fold 2
Mean Squared Error: 0.8600949629501164
Correlation Coeff: 0.9675026105307813
Coeff of Determination: 0.935337447188353
Fold 3
Mean Squared Error: 0.7427796321330984
Correlation Coeff: 0.9727679622659688
Coeff of Determination: 0.9461875493861901
Mean Coeff of Determination: 0.9388490699958787
##### nfoldCV Regression Forest #####
Fold 1
Mean Squared Error: 1.2274348325856186
Correlation Coeff: 0.9704260879283352
Coeff of Determination: 0.9140860432926056
Fold 2
Mean Squared Error: 1.010177816120529
Correlation Coeff: 0.9709054128303982
Coeff of Determination: 0.9278989317966039
Fold 3
Mean Squared Error: 1.01890639749946
Correlation Coeff: 0.9658631947543498
Coeff of Determination: 0.9193339316722111
Mean Coeff of Determination: 0.9204396355871403
==================================================
TEST: regression/digits.jl
==================================================
TEST: regression/scikitlearn.jl
==================================================
Test Summary: | Pass Total
Test Suites | 135 135
Testing DecisionTree tests passed