Package evaluation to test ModalDecisionTrees on Julia 1.11.7 (58327cce5e*) started at 2025-10-29T06:19:40.660


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# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 8.51s


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# Installation
#

Installing ModalDecisionTrees...
   Resolving package versions...
    Updating `~/.julia/environments/v1.11/Project.toml`
  [e54bda2e] + ModalDecisionTrees v0.5.2
    Updating `~/.julia/environments/v1.11/Manifest.toml`
  [da404889] + ARFFFiles v1.5.0
  [1520ce14] + AbstractTrees v0.4.5
  [79e6a3ab] + Adapt v4.4.0
  [66dad0bd] + AliasTables v1.1.3
  [ec485272] + ArnoldiMethod v0.4.0
  [4fba245c] + ArrayInterface v7.22.0
  [d1d4a3ce] + BitFlags v0.1.9
  [336ed68f] + CSV v0.10.15
  [acdeb78f] + Catch22 v0.7.0
⌅ [324d7699] + CategoricalArrays v0.10.8
⌅ [af321ab8] + CategoricalDistributions v0.1.15
  [da1fd8a2] + CodeTracking v2.0.1
  [944b1d66] + CodecZlib v0.7.8
  [3da002f7] + ColorTypes v0.12.1
  [34da2185] + Compat v4.18.1
  [807dbc54] + Compiler v0.1.1
  [f0e56b4a] + ConcurrentUtilities v2.5.0
  [187b0558] + ConstructionBase v1.6.0
  [a8cc5b0e] + Crayons v4.1.1
  [9a962f9c] + DataAPI v1.16.0
  [a93c6f00] + DataFrames v1.8.1
⌅ [864edb3b] + DataStructures v0.18.22
  [e2d170a0] + DataValueInterfaces v1.0.0
  [e7dc6d0d] + DataValues v0.4.13
  [8bb1440f] + DelimitedFiles v1.9.1
  [85a47980] + Dictionaries v0.4.5
  [0703355e] + DimensionalData v0.29.24
  [6e83dbb3] + Discretizers v3.2.4
  [31c24e10] + Distributions v0.25.122
  [ffbed154] + DocStringExtensions v0.9.5
  [460bff9d] + ExceptionUnwrapping v0.1.11
  [411431e0] + Extents v0.1.6
  [48062228] + FilePathsBase v0.9.24
  [1a297f60] + FillArrays v1.14.0
  [53c48c17] + FixedPointNumbers v0.8.5
  [069b7b12] + FunctionWrappers v1.1.3
  [86223c79] + Graphs v1.13.1
  [cd3eb016] + HTTP v1.10.19
  [34004b35] + HypergeometricFunctions v0.3.28
  [313cdc1a] + Indexing v1.1.1
  [d25df0c9] + Inflate v0.1.5
  [842dd82b] + InlineStrings v1.4.5
  [85a1e053] + Interfaces v0.3.2
  [8197267c] + IntervalSets v0.7.11
  [41ab1584] + InvertedIndices v1.3.1
  [92d709cd] + IrrationalConstants v0.2.6
  [c8e1da08] + IterTools v1.10.0
  [1c8ee90f] + IterableTables v1.0.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.7.1
⌅ [682c06a0] + JSON v0.21.4
  [aa1ae85d] + JuliaInterpreter v0.10.6
  [b964fa9f] + LaTeXStrings v1.4.0
  [50d2b5c4] + Lazy v0.15.1
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e6f89c97] + LoggingExtras v1.2.0
  [6f1432cf] + LoweredCodeUtils v3.4.4
  [e80e1ace] + MLJModelInterface v1.12.0
  [1914dd2f] + MacroTools v0.5.16
  [739be429] + MbedTLS v1.1.9
  [6fafb56a] + Memoization v0.2.2
  [e1d29d7a] + Missings v1.2.0
  [e54bda2e] + ModalDecisionTrees v0.5.2
  [8cc5100c] + MultiData v0.1.4
  [8b6db2d4] + OpenML v0.3.2
  [4d8831e6] + OpenSSL v1.5.0
  [bac558e1] + OrderedCollections v1.8.1
  [90014a1f] + PDMats v0.11.36
  [69de0a69] + Parsers v2.8.3
  [2dfb63ee] + PooledArrays v1.4.3
⌅ [aea7be01] + PrecompileTools v1.2.1
  [21216c6a] + Preferences v1.5.0
⌅ [08abe8d2] + PrettyTables v2.4.0
  [33c8b6b6] + ProgressLogging v0.1.5
  [92933f4c] + ProgressMeter v1.11.0
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [1a8c2f83] + Query v1.0.0
  [2aef5ad7] + QueryOperators v0.9.3
  [3cdcf5f2] + RecipesBase v1.3.4
  [189a3867] + Reexport v1.2.2
  [ae029012] + Requires v1.3.1
  [c5292f4c] + ResumableFunctions v1.0.4
  [295af30f] + Revise v3.11.0
  [79098fc4] + Rmath v0.9.0
  [321657f4] + ScientificTypes v3.1.1
  [30f210dd] + ScientificTypesBase v3.0.0
  [6c6a2e73] + Scratch v1.3.0
  [91c51154] + SentinelArrays v1.4.8
  [777ac1f9] + SimpleBufferStream v1.2.0
  [699a6c99] + SimpleTraits v0.9.5
  [4475fa32] + SoleBase v0.13.4
  [123f1ae1] + SoleData v0.16.5
  [b002da8f] + SoleLogics v0.13.2
  [4249d9c7] + SoleModels v0.10.6
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.6.1
  [90137ffa] + StaticArrays v1.9.15
  [1e83bf80] + StaticArraysCore v1.4.4
  [64bff920] + StatisticalTraits v3.5.0
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.7.1
  [2913bbd2] + StatsBase v0.34.7
  [4c63d2b9] + StatsFuns v1.5.2
  [892a3eda] + StringManipulation v0.4.1
  [5e66a065] + TableShowUtils v0.2.6
  [3783bdb8] + TableTraits v1.0.1
  [382cd787] + TableTraitsUtils v1.0.2
  [bd369af6] + Tables v1.12.1
  [4239201d] + ThreadSafeDicts v0.1.6
  [f3112013] + TimeseriesFeatures v0.6.1
  [3bb67fe8] + TranscodingStreams v0.11.3
  [5c2747f8] + URIs v1.6.1
  [2fbcfb34] + UniqueVectors v1.2.0
  [ea10d353] + WeakRefStrings v1.4.2
  [76eceee3] + WorkerUtilities v1.6.1
  [a5390f91] + ZipFile v0.10.1
  [458c3c95] + OpenSSL_jll v3.5.4+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [8a07c0c5] + catch22_jll v0.5.0+0
  [0dad84c5] + ArgTools v1.1.2
  [56f22d72] + Artifacts v1.11.0
  [2a0f44e3] + Base64 v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8ba89e20] + Distributed v1.11.0
  [f43a241f] + Downloads v1.6.0
  [7b1f6079] + FileWatching v1.11.0
  [9fa8497b] + Future v1.11.0
  [b77e0a4c] + InteractiveUtils v1.11.0
  [4af54fe1] + LazyArtifacts v1.11.0
  [b27032c2] + LibCURL v0.6.4
  [76f85450] + LibGit2 v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.11.0
  [56ddb016] + Logging v1.11.0
  [d6f4376e] + Markdown v1.11.0
  [a63ad114] + Mmap v1.11.0
  [ca575930] + NetworkOptions v1.2.0
  [44cfe95a] + Pkg v1.11.0
  [de0858da] + Printf v1.11.0
  [3fa0cd96] + REPL v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v0.7.0
  [9e88b42a] + Serialization v1.11.0
  [1a1011a3] + SharedArrays v1.11.0
  [6462fe0b] + Sockets v1.11.0
  [2f01184e] + SparseArrays v1.11.0
  [f489334b] + StyledStrings v1.11.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [a4e569a6] + Tar v1.10.0
  [8dfed614] + Test v1.11.0
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0
  [deac9b47] + LibCURL_jll v8.6.0+0
  [e37daf67] + LibGit2_jll v1.7.2+0
  [29816b5a] + LibSSH2_jll v1.11.0+1
  [c8ffd9c3] + MbedTLS_jll v2.28.6+0
  [14a3606d] + MozillaCACerts_jll v2023.12.12
  [4536629a] + OpenBLAS_jll v0.3.27+1
  [05823500] + OpenLibm_jll v0.8.5+0
  [bea87d4a] + SuiteSparse_jll v7.7.0+0
  [83775a58] + Zlib_jll v1.2.13+1
  [8e850b90] + libblastrampoline_jll v5.11.0+0
  [8e850ede] + nghttp2_jll v1.59.0+0
  [3f19e933] + p7zip_jll v17.4.0+2
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated -m`

Installation completed after 5.7s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 378.36s


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# Testing
#

     Testing ModalDecisionTrees
      Status `/tmp/jl_F8jt1p/Project.toml`
  [da404889] ARFFFiles v1.5.0
  [1520ce14] AbstractTrees v0.4.5
⌅ [324d7699] CategoricalArrays v0.10.8
⌅ [af321ab8] CategoricalDistributions v0.1.15
  [e3df1716] D3Trees v0.3.5
  [a93c6f00] DataFrames v1.8.1
⌅ [864edb3b] DataStructures v0.18.22
  [31c24e10] Distributions v0.25.122
  [1a297f60] FillArrays v1.14.0
  [069b7b12] FunctionWrappers v1.1.3
  [cd3eb016] HTTP v1.10.19
  [6a3955dd] ImageFiltering v0.7.12
  [916415d5] Images v0.26.2
  [50d2b5c4] Lazy v0.15.1
  [eb30cadb] MLDatasets v0.7.18
  [add582a8] MLJ v0.21.0
  [a7f614a8] MLJBase v1.9.2
  [e80e1ace] MLJModelInterface v1.12.0
  [6fafb56a] Memoization v0.2.2
  [e54bda2e] ModalDecisionTrees v0.5.2
  [8cc5100c] MultiData v0.1.4
  [8b6db2d4] OpenML v0.3.2
  [7f904dfe] PlutoUI v0.7.73
  [92933f4c] ProgressMeter v1.11.0
  [ce6b1742] RDatasets v0.7.7
  [189a3867] Reexport v1.2.2
  [c5292f4c] ResumableFunctions v1.0.4
  [321657f4] ScientificTypes v3.1.1
  [4475fa32] SoleBase v0.13.4 `https://github.com/aclai-lab/SoleBase.jl#devModal`
  [123f1ae1] SoleData v0.16.5
  [b002da8f] SoleLogics v0.13.2
  [4249d9c7] SoleModels v0.10.6
  [2913bbd2] StatsBase v0.34.7
  [bd369af6] Tables v1.12.1
  [a5390f91] ZipFile v0.10.1
  [b77e0a4c] InteractiveUtils v1.11.0
  [37e2e46d] LinearAlgebra v1.11.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_F8jt1p/Manifest.toml`
  [da404889] ARFFFiles v1.5.0
  [621f4979] AbstractFFTs v1.5.0
  [6e696c72] AbstractPlutoDingetjes v1.3.2
  [1520ce14] AbstractTrees v0.4.5
  [7d9f7c33] Accessors v0.1.42
  [79e6a3ab] Adapt v4.4.0
  [66dad0bd] AliasTables v1.1.3
  [dce04be8] ArgCheck v2.5.0
  [ec485272] ArnoldiMethod v0.4.0
  [4fba245c] ArrayInterface v7.22.0
  [a9b6321e] Atomix v1.1.2
  [a963bdd2] AtomsBase v0.5.2
  [13072b0f] AxisAlgorithms v1.1.0
  [39de3d68] AxisArrays v0.4.8
⌅ [ab4f0b2a] BFloat16s v0.5.1
  [198e06fe] BangBang v0.4.6
  [9718e550] Baselet v0.1.1
  [50ba71b6] BitBasis v0.9.10
  [d1d4a3ce] BitFlags v0.1.9
  [62783981] BitTwiddlingConvenienceFunctions v0.1.6
  [e1450e63] BufferedStreams v1.2.2
  [fa961155] CEnum v0.5.0
  [2a0fbf3d] CPUSummary v0.2.7
  [336ed68f] CSV v0.10.15
  [aafaddc9] CatIndices v0.2.2
  [acdeb78f] Catch22 v0.7.0
⌅ [324d7699] CategoricalArrays v0.10.8
⌅ [af321ab8] CategoricalDistributions v0.1.15
  [d360d2e6] ChainRulesCore v1.26.0
  [46823bd8] Chemfiles v0.10.43
  [fb6a15b2] CloseOpenIntervals v0.1.13
  [aaaa29a8] Clustering v0.15.8
  [da1fd8a2] CodeTracking v2.0.1
  [944b1d66] CodecZlib v0.7.8
  [35d6a980] ColorSchemes v3.31.0
  [3da002f7] ColorTypes v0.12.1
  [c3611d14] ColorVectorSpace v0.11.0
  [5ae59095] Colors v0.13.1
  [861a8166] Combinatorics v1.0.3
  [f70d9fcc] CommonWorldInvalidations v1.0.0
  [34da2185] Compat v4.18.1
  [807dbc54] Compiler v0.1.1
  [a33af91c] CompositionsBase v0.1.2
  [ed09eef8] ComputationalResources v0.3.2
  [f0e56b4a] ConcurrentUtilities v2.5.0
  [187b0558] ConstructionBase v1.6.0
  [6add18c4] ContextVariablesX v0.1.3
  [150eb455] CoordinateTransformations v0.6.4
  [adafc99b] CpuId v0.3.1
  [a8cc5b0e] Crayons v4.1.1
  [dc8bdbbb] CustomUnitRanges v1.0.2
  [e3df1716] D3Trees v0.3.5
  [9a962f9c] DataAPI v1.16.0
  [124859b0] DataDeps v0.7.13
  [a93c6f00] DataFrames v1.8.1
⌅ [864edb3b] DataStructures v0.18.22
  [e2d170a0] DataValueInterfaces v1.0.0
  [e7dc6d0d] DataValues v0.4.13
  [244e2a9f] DefineSingletons v0.1.2
  [8bb1440f] DelimitedFiles v1.9.1
  [85a47980] Dictionaries v0.4.5
  [0703355e] DimensionalData v0.29.24
  [6e83dbb3] Discretizers v3.2.4
  [b4f34e82] Distances v0.10.12
  [31c24e10] Distributions v0.25.122
  [ffbed154] DocStringExtensions v0.9.5
  [792122b4] EarlyStopping v0.3.0
  [460bff9d] ExceptionUnwrapping v0.1.11
  [e2ba6199] ExprTools v0.1.10
  [411431e0] Extents v0.1.6
  [4f61f5a4] FFTViews v0.3.2
  [7a1cc6ca] FFTW v1.10.0
  [cc61a311] FLoops v0.2.2
  [b9860ae5] FLoopsBase v0.1.1
  [33837fe5] FeatureSelection v0.2.3
  [5789e2e9] FileIO v1.17.1
  [48062228] FilePathsBase v0.9.24
  [1a297f60] FillArrays v1.14.0
  [53c48c17] FixedPointNumbers v0.8.5
  [069b7b12] FunctionWrappers v1.1.3
  [46192b85] GPUArraysCore v0.2.0
  [92fee26a] GZip v0.6.2
  [c27321d9] Glob v1.3.1
  [a2bd30eb] Graphics v1.1.3
  [86223c79] Graphs v1.13.1
  [f67ccb44] HDF5 v0.17.2
  [cd3eb016] HTTP v1.10.19
  [076d061b] HashArrayMappedTries v0.2.0
  [2c695a8d] HistogramThresholding v0.3.1
  [3e5b6fbb] HostCPUFeatures v0.1.17
  [34004b35] HypergeometricFunctions v0.3.28
  [47d2ed2b] Hyperscript v0.0.5
  [ac1192a8] HypertextLiteral v0.9.5
  [b5f81e59] IOCapture v1.0.0
  [615f187c] IfElse v0.1.1
  [2803e5a7] ImageAxes v0.6.12
  [c817782e] ImageBase v0.1.7
  [cbc4b850] ImageBinarization v0.3.1
  [f332f351] ImageContrastAdjustment v0.3.12
  [a09fc81d] ImageCore v0.10.5
  [89d5987c] ImageCorners v0.1.3
  [51556ac3] ImageDistances v0.2.17
  [6a3955dd] ImageFiltering v0.7.12
  [82e4d734] ImageIO v0.6.9
  [6218d12a] ImageMagick v1.4.2
  [bc367c6b] ImageMetadata v0.9.10
⌃ [787d08f9] ImageMorphology v0.4.6
  [2996bd0c] ImageQualityIndexes v0.3.7
⌃ [80713f31] ImageSegmentation v1.9.0
  [4e3cecfd] ImageShow v0.3.8
  [02fcd773] ImageTransformations v0.10.2
  [916415d5] Images v0.26.2
  [313cdc1a] Indexing v1.1.1
  [9b13fd28] IndirectArrays v1.0.0
  [d25df0c9] Inflate v0.1.5
  [22cec73e] InitialValues v0.3.1
  [842dd82b] InlineStrings v1.4.5
  [1d092043] IntegralArrays v0.1.6
  [85a1e053] Interfaces v0.3.2
  [7d512f48] InternedStrings v0.7.0
  [a98d9a8b] Interpolations v0.16.2
  [8197267c] IntervalSets v0.7.11
  [3587e190] InverseFunctions v0.1.17
  [41ab1584] InvertedIndices v1.3.1
  [92d709cd] IrrationalConstants v0.2.6
  [c8e1da08] IterTools v1.10.0
  [1c8ee90f] IterableTables v1.0.0
  [b3c1a2ee] IterationControl v0.5.4
  [82899510] IteratorInterfaceExtensions v1.0.0
⌅ [033835bb] JLD2 v0.5.15
  [692b3bcd] JLLWrappers v1.7.1
⌅ [682c06a0] JSON v0.21.4
  [0f8b85d8] JSON3 v1.14.3
  [b835a17e] JpegTurbo v0.1.6
  [aa1ae85d] JuliaInterpreter v0.10.6
  [b14d175d] JuliaVariables v0.2.4
  [63c18a36] KernelAbstractions v0.9.38
  [b964fa9f] LaTeXStrings v1.4.0
  [a5e1c1ea] LatinHypercubeSampling v1.9.0
  [10f19ff3] LayoutPointers v0.1.17
  [50d2b5c4] Lazy v0.15.1
  [8cdb02fc] LazyModules v0.3.1
⌅ [92ad9a40] LearnAPI v1.0.1
  [2ab3a3ac] LogExpFunctions v0.3.29
  [e6f89c97] LoggingExtras v1.2.0
  [bdcacae8] LoopVectorization v0.12.173
  [6f1432cf] LoweredCodeUtils v3.4.4
  [23992714] MAT v0.10.7
  [6c6e2e6c] MIMEs v1.1.0
  [c2834f40] MLCore v1.0.0
  [eb30cadb] MLDatasets v0.7.18
⌅ [64a0f543] MLFlowClient v0.5.1
  [add582a8] MLJ v0.21.0
  [45f359ea] MLJBalancing v0.1.5
  [a7f614a8] MLJBase v1.9.2
  [50ed68f4] MLJEnsembles v0.4.3
  [7b7b8358] MLJFlow v0.5.0
  [614be32b] MLJIteration v0.6.3
  [e80e1ace] MLJModelInterface v1.12.0
  [d491faf4] MLJModels v0.18.1
  [23777cdb] MLJTransforms v0.1.3
  [03970b2e] MLJTuning v0.8.8
  [d8e11817] MLStyle v0.4.17
  [f1d291b0] MLUtils v0.4.8
  [3da0fdf6] MPIPreferences v0.1.11
  [1914dd2f] MacroTools v0.5.16
  [d125e4d3] ManualMemory v0.1.8
  [dbb5928d] MappedArrays v0.4.2
  [739be429] MbedTLS v1.1.9
  [6fafb56a] Memoization v0.2.2
  [626554b9] MetaGraphs v0.8.1
  [128add7d] MicroCollections v0.2.0
  [e1d29d7a] Missings v1.2.0
  [78c3b35d] Mocking v0.8.1
  [e54bda2e] ModalDecisionTrees v0.5.2
  [e94cdb99] MosaicViews v0.3.4
  [8cc5100c] MultiData v0.1.4
  [872c559c] NNlib v0.9.31
  [15e1cf62] NPZ v0.4.3
  [77ba4419] NaNMath v1.1.3
  [71a1bf82] NameResolution v0.1.5
  [b8a86587] NearestNeighbors v0.4.22
  [f09324ee] Netpbm v1.1.1
  [6fe1bfb0] OffsetArrays v1.17.0
  [52e1d378] OpenEXR v0.3.3
  [8b6db2d4] OpenML v0.3.2
  [4d8831e6] OpenSSL v1.5.0
  [bac558e1] OrderedCollections v1.8.1
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⌅ [02c8fc9c] XML2_jll v2.13.9+0
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        Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading.
     Testing Running tests...
WARNING: Method definition _dummy_backedge() in module Memoization at /home/pkgeval/.julia/packages/Memoization/ON3Za/src/Memoization.jl:49 overwritten at /home/pkgeval/.julia/packages/Memoization/ON3Za/src/Memoization.jl:65.
Julia version: 1.11.7

##################################################
TEST: base.jl
┌ Warning: Parity encountered in bestguess! counts (2 elements): Dict("Class_1" => 1, "Class_2" => 1), argmax: Class_1, max: 1 (sum = 2)
└ @ SoleBase ~/.julia/packages/SoleBase/KRa1C/src/machine-learning-utils.jl:96
┌ Warning: Parity encountered in bestguess! counts (2 elements): Dict("Class_1" => 1, "Class_2" => 1), argmax: Class_1, max: 1 (sum = 2)
└ @ SoleBase ~/.julia/packages/SoleBase/KRa1C/src/machine-learning-utils.jl:96
==================================================

##################################################
TEST: classification/japanesevowels.jl
Precompiling MLJ...
   9071.7 ms  ✓ KernelAbstractions
   2283.5 ms  ✓ LatinHypercubeSampling
   3533.7 ms  ✓ MLFlowClient
  16183.0 ms  ✓ MLJTransforms
  24217.4 ms  ✓ MLJModels
   2268.3 ms  ✓ KernelAbstractions → SparseArraysExt
   1742.1 ms  ✓ KernelAbstractions → LinearAlgebraExt
  14324.4 ms  ✓ NNlib
   3095.2 ms  ✓ NNlib → NNlibSpecialFunctionsExt
  18746.2 ms  ✓ MLUtils
  16706.2 ms  ✓ StatisticalMeasuresBase
  45400.7 ms  ✓ StatisticalMeasures
  20601.9 ms  ✓ MLJBase
  14956.8 ms  ✓ MLJEnsembles
  10438.9 ms  ✓ StatisticalMeasures → ScientificTypesExt
  16272.9 ms  ✓ MLJBalancing
  12203.4 ms  ✓ MLJBase → DefaultMeasuresExt
  18193.0 ms  ✓ MLJIteration
  16438.8 ms  ✓ MLJTuning
  15315.6 ms  ✓ MLJFlow
  23893.7 ms  ✓ MLJ
  21 dependencies successfully precompiled in 314 seconds. 190 already precompiled.
Precompiling BangBangDataFramesExt...
   5298.1 ms  ✓ BangBang → BangBangDataFramesExt
  1 dependency successfully precompiled in 7 seconds. 45 already precompiled.
Precompiling TransducersDataFramesExt...
   5219.1 ms  ✓ Transducers → TransducersDataFramesExt
  1 dependency successfully precompiled in 7 seconds. 60 already precompiled.
Precompiling DimensionalDataChainRulesCoreExt...
   1685.7 ms  ✓ DimensionalData → DimensionalDataChainRulesCoreExt
  1 dependency successfully precompiled in 2 seconds. 33 already precompiled.
[ Info: Downloading dataset 375.
┌ Warning: Base.download is deprecated; use Downloads.download instead
│   caller = load(id::Int64; maxbytes::Nothing) at data.jl:73
└ @ OpenML ~/.julia/packages/OpenML/cONhN/src/data.jl:73
┌ Warning: Conversion to OrderedDict is deprecated for unordered associative containers (in this case, Dict{Any, Any}). Use an ordered or sorted associative type, such as SortedDict and OrderedDict.
│   caller = DictColumnTable at dicts.jl:3 [inlined]
└ @ Core ~/.julia/packages/Tables/cRTb7/src/dicts.jl:3
┌ Warning: Conversion to OrderedDict is deprecated for unordered associative containers (in this case, Dict{Symbol, AbstractVector}). Use an ordered or sorted associative type, such as SortedDict and OrderedDict.
│   caller = DictColumnTable at dicts.jl:3 [inlined]
└ @ Core ~/.julia/packages/Tables/cRTb7/src/dicts.jl:3
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
166.930257 seconds (121.45 M allocations: 6.282 GiB, 1.45% gc time, 97.76% compilation time)
▣ {1}(⟨G⟩(min[A] < 0.92))
├✔ {1}(⟨G⟩((min[A] < 0.92) ∧ (max[B] ≥ -0.41)))
│ ├✔ {1}(⟨G⟩((min[A] < 0.92) ∧ (max[B] ≥ -0.41) ∧ (min[G] ≥ -0.15)))
│ │ ├✔ 3
│ │ └✘ 4
│ └✘ {1}(⟨G⟩((min[A] < 0.92) ∧ (min[J] ≥ -0.07)))
│   ├✔ 3
│   └✘ 2
└✘ 1
▣ {1}(⟨G⟩(min[a] < 0.91504))
├✔ {1}(⟨G⟩((min[a] < 0.91504) ∧ (max[b] ≥ -0.408784)))
│ ├✔variable_names_map = ["a", "b"]
 {1}(⟨G⟩((min[a] < 0.91504) ∧ (max[b] ≥ -0.408784) ∧ (min[?V7?] ≥ -0.150238)))
│ │ ├✔ 3
│ │ └✘ 4
│ └✘variable_names_map = ["a", "b"]
 {1}(⟨G⟩((min[a] < 0.91504) ∧ (min[?V10?] ≥ -0.066248)))
│   ├✔ 3
│   └✘ 2
└✘ 1
▣ {1}(⟨G⟩(min[a] < 0.91504))
├✔ {1}(⟨G⟩((min[a] < 0.91504) ∧ (max[b] ≥ -0.408784)))
│ ├✔variable_names_map = ["a", "b"]
 {1}(⟨G⟩((min[a] < 0.91504) ∧ (max[b] ≥ -0.408784) ∧ (min[?V7?] ≥ -0.150238)))
│ │ ├✔ 3
│ │ └✘ 4
│ └✘variable_names_map = ["a", "b"]
 {1}(⟨G⟩((min[a] < 0.91504) ∧ (min[?V10?] ≥ -0.066248)))
│   ├✔ 3
│   └✘ 2
└✘ 1
▣ {1}(⟨G⟩(min[A] < 0.91504))
├✔ {1}(⟨G⟩((min[A] < 0.91504) ∧ (max[B] ≥ -0.408784)))
│ ├✔ {1}(⟨G⟩((min[A] < 0.91504) ∧ (max[B] ≥ -0.408784) ∧ (min[G] ≥ -0.150238)))
│ │ ├✔ 3
│ │ └✘ 4
│ └✘ {1}(⟨G⟩((min[A] < 0.91504) ∧ (min[J] ≥ -0.066248)))
│   ├✔ 3
│   └✘ 2
└✘ 1
▣ {1}(⟨G⟩(min[A] < 0.91504))
├✔ {1}(⟨G⟩((min[A] < 0.91504) ∧ (max[B] ≥ -0.408784)))
│ ├✔ {1}(⟨G⟩((min[A] < 0.91504) ∧ (max[B] ≥ -0.408784) ∧ (min[G] ≥ -0.150238)))
│ │ ├✔ 3
│ │ └✘ 4
│ └✘ {1}(⟨G⟩((min[A] < 0.91504) ∧ (min[J] ≥ -0.066248)))
│   ├✔ 3
│   └✘ 2
└✘ 1
▣ {1}(⟨G⟩(min[V1] < 0.91504))
├✔ {1}(⟨G⟩((min[V1] < 0.91504) ∧ (max[V2] ≥ -0.408784)))
│ ├✔ {1}(⟨G⟩((min[V1] < 0.91504) ∧ (max[V2] ≥ -0.408784) ∧ (min[V7] ≥ -0.150238)))
│ │ ├✔ 3
│ │ └✘ 4
│ └✘ {1}(⟨G⟩((min[V1] < 0.91504) ∧ (min[V10] ≥ -0.066248)))
│   ├✔ 3
│   └✘ 2
└✘ 1
▣ (⟨G⟩(min[1] < 0.91504))
├✔ (⟨G⟩((min[1] < 0.91504) ∧ (max[2] ≥ -0.408784)))
│ ├✔ (⟨G⟩((min[1] < 0.91504) ∧ (max[2] ≥ -0.408784) ∧ (min[7] ≥ -0.150238)))
│ │ ├✔ 3
│ │ └✘ 4
│ └✘ (⟨G⟩((min[1] < 0.91504) ∧ (min[10] ≥ -0.066248)))
│   ├✔ 3
│   └✘ 2
└✘ 1
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
 38.755526 seconds (39.55 M allocations: 1.583 GiB, 1.13% gc time, 91.51% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
  3.808658 seconds (27.70 M allocations: 996.174 MiB, 5.94% gc time, 9.59% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
 11.995045 seconds (22.76 M allocations: 840.083 MiB, 1.32% gc time, 79.26% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
  1.127113 seconds (7.12 M allocations: 217.002 MiB, 6.92% gc time, 17.85% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
  9.695564 seconds (15.41 M allocations: 583.439 MiB, 9.37% gc time, 74.51% compilation time)
┌ Warning: An absolute n_subfeatures was provided 2. It is recommended to use relative values (between 0 and 1), interpreted as the share of the random portion of feature space explored at each split.
└ @ ModalDecisionTrees.MLJInterface ~/.julia/packages/ModalDecisionTrees/WJRxy/src/interfaces/MLJ/ModalDecisionTree.jl:120
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
 12.577021 seconds (13.07 M allocations: 521.388 MiB, 1.37% gc time, 90.22% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
  6.685462 seconds (19.51 M allocations: 717.350 MiB, 2.10% gc time, 66.83% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
  3.938536 seconds (27.95 M allocations: 1011.173 MiB, 4.98% gc time, 13.20% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
┌ Error: Problem fitting the machine machine(ModalDecisionTree(max_depth = nothing, …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/GY2fM/src/machines.jl:695
[ Info: Running type checks... 
[ Info: Type checks okay. 
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
┌ Error: Problem fitting the machine machine(ModalDecisionTree(max_depth = nothing, …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/GY2fM/src/machines.jl:695
[ Info: Running type checks... 
[ Info: Type checks okay. 
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
┌ Error: Problem fitting the machine machine(ModalDecisionTree(max_depth = nothing, …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/GY2fM/src/machines.jl:695
[ Info: Running type checks... 
[ Info: Type checks okay. 
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
┌ Error: Problem fitting the machine machine(ModalDecisionTree(max_depth = nothing, …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/GY2fM/src/machines.jl:695
[ Info: Running type checks... 
[ Info: Type checks okay. 
┌ Warning: AbstractArray of 3 dimensions and size (7, 12, 640) encountered. This will be interpreted as a dataset of 640 instances, 12 variables, and channel size (7,).
└ @ SoleData ~/.julia/packages/SoleData/XeyXX/src/utils/autologiset-tools.jl:184
[ Info: Precomputing logiset...

Computing logiset...   0%|▏                              |  ETA: 0:07:05
Computing logiset... 100%|███████████████████████████████| Time: 0:00:03
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
 38.815162 seconds (219.25 M allocations: 8.885 GiB, 3.61% gc time, 26.76% compilation time)
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
 24.676966 seconds (174.64 M allocations: 7.017 GiB, 4.36% gc time, 2.33% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalAdaBoost(max_depth = 1, …), …).

Applying trees...   8%|██▊                               |  ETA: 0:00:39
Applying trees... 100%|██████████████████████████████████| Time: 0:00:03
▣ Ensemble{String} of 25 models of type DecisionTree{String}
├[1/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.92))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[2/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.41))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[3/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.92))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[4/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.56))
│      ├✔ 2 : (ninstances = 48, ncovered = 48, confidence = 0.5, lift = 1.0)
│      └✘ 4 : (ninstances = 32, ncovered = 32, confidence = 0.19, lift = 1.0)
├[5/25]┐ {1}(⟨G⟩(minimum⁺[A] ≥ 1.32))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│      └✘ 3 : (ninstances = 60, ncovered = 60, confidence = 0.42, lift = 1.0)
├[6/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.41))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[7/25]┐ {1}(⟨G⟩(minimum⁻[A] < 1.02))
│      ├✔ 4 : (ninstances = 58, ncovered = 58, confidence = 0.14, lift = 1.0)
│      └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[8/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.34))
│      ├✔ 3 : (ninstances = 48, ncovered = 48, confidence = 0.46, lift = 1.0)
│      └✘ 2 : (ninstances = 32, ncovered = 32, confidence = 0.78, lift = 1.0)
├[9/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.43))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│      └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[10/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.92))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[11/25]┐ {1}(⟨G⟩(minimum⁺[J] ≥ -0.14))
│       ├✔ 4 : (ninstances = 39, ncovered = 39, confidence = 0.15, lift = 1.0)
│       └✘ 2 : (ninstances = 41, ncovered = 41, confidence = 0.54, lift = 1.0)
├[12/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.92))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[13/25]┐ {1}(⟨G⟩(minimum⁻[I] < -0.24))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[14/25]┐ {1}(⟨G⟩(minimum⁻[F] < -0.58))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
├[15/25]┐ {1}(⟨G⟩(minimum⁻[D] < -0.57))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.86, lift = 1.0)
├[16/25]┐ {1}(⟨G⟩(minimum⁺[D] ≥ -0.45))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.24, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[17/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.92))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[18/25]┐ {1}(⟨G⟩(minimum⁺[I] ≥ 0.08))
│       ├✔ 4 : (ninstances = 14, ncovered = 14, confidence = 0.43, lift = 1.0)
│       └✘ 2 : (ninstances = 66, ncovered = 66, confidence = 0.38, lift = 1.0)
├[19/25]┐ {1}(⟨G⟩(minimum⁻[A] < 1.02))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[20/25]┐ {1}(⟨G⟩(minimum⁺[D] ≥ -0.44))
│       ├✔ 1 : (ninstances = 44, ncovered = 44, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 36, ncovered = 36, confidence = 0.22, lift = 1.0)
├[21/25]┐ {1}(⟨G⟩(minimum⁻[B] < -0.62))
│       ├✔ 2 : (ninstances = 33, ncovered = 33, confidence = 0.76, lift = 1.0)
│       └✘ 3 : (ninstances = 47, ncovered = 47, confidence = 0.47, lift = 1.0)
├[22/25]┐ {1}(⟨G⟩(minimum⁺[A] ≥ 1.32))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[23/25]┐ {1}(⟨G⟩(minimum⁺[J] ≥ -0.07))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.73, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
├[24/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.43))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
└[25/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.21))
        ├✔ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
        └✘ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
▣ Ensemble{String} of 25 models of type DecisionTree{String}
├[1/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[2/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[3/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[4/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.562459))
│      ├✔ 2 : (ninstances = 48, ncovered = 48, confidence = 0.5, lift = 1.0)
│      └✘ 4 : (ninstances = 32, ncovered = 32, confidence = 0.19, lift = 1.0)
├[5/25]┐ {1}(⟨G⟩(minimum⁺[A] ≥ 1.320922))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│      └✘ 3 : (ninstances = 60, ncovered = 60, confidence = 0.42, lift = 1.0)
├[6/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[7/25]┐ {1}(⟨G⟩(minimum⁻[A] < 1.019561))
│      ├✔ 4 : (ninstances = 58, ncovered = 58, confidence = 0.14, lift = 1.0)
│      └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[8/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.343207))
│      ├✔ 3 : (ninstances = 48, ncovered = 48, confidence = 0.46, lift = 1.0)
│      └✘ 2 : (ninstances = 32, ncovered = 32, confidence = 0.78, lift = 1.0)
├[9/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.434608))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│      └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[10/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[11/25]┐ {1}(⟨G⟩(minimum⁺[J] ≥ -0.144559))
│       ├✔ 4 : (ninstances = 39, ncovered = 39, confidence = 0.15, lift = 1.0)
│       └✘ 2 : (ninstances = 41, ncovered = 41, confidence = 0.54, lift = 1.0)
├[12/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[13/25]┐ {1}(⟨G⟩(minimum⁻[I] < -0.243571))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[14/25]┐ {1}(⟨G⟩(minimum⁻[F] < -0.575495))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
├[15/25]┐ {1}(⟨G⟩(minimum⁻[D] < -0.568272))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.86, lift = 1.0)
├[16/25]┐ {1}(⟨G⟩(minimum⁺[D] ≥ -0.446165))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.24, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[17/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[18/25]┐ {1}(⟨G⟩(minimum⁺[I] ≥ 0.083275))
│       ├✔ 4 : (ninstances = 14, ncovered = 14, confidence = 0.43, lift = 1.0)
│       └✘ 2 : (ninstances = 66, ncovered = 66, confidence = 0.38, lift = 1.0)
├[19/25]┐ {1}(⟨G⟩(minimum⁻[A] < 1.019561))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[20/25]┐ {1}(⟨G⟩(minimum⁺[D] ≥ -0.4391))
│       ├✔ 1 : (ninstances = 44, ncovered = 44, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 36, ncovered = 36, confidence = 0.22, lift = 1.0)
├[21/25]┐ {1}(⟨G⟩(minimum⁻[B] < -0.621772))
│       ├✔ 2 : (ninstances = 33, ncovered = 33, confidence = 0.76, lift = 1.0)
│       └✘ 3 : (ninstances = 47, ncovered = 47, confidence = 0.47, lift = 1.0)
├[22/25]┐ {1}(⟨G⟩(minimum⁺[A] ≥ 1.320922))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[23/25]┐ {1}(⟨G⟩(minimum⁺[J] ≥ -0.068057))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.73, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
├[24/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.434608))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
└[25/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.212466))
        ├✔ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
        └✘ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
▣ Ensemble{String} of 25 models of type DecisionTree{String}
├[1/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[2/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[3/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[4/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.562459))
│      ├✔ 2 : (ninstances = 48, ncovered = 48, confidence = 0.5, lift = 1.0)
│      └✘ 4 : (ninstances = 32, ncovered = 32, confidence = 0.19, lift = 1.0)
├[5/25]┐ {1}(⟨G⟩(minimum⁺[A] ≥ 1.320922))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│      └✘ 3 : (ninstances = 60, ncovered = 60, confidence = 0.42, lift = 1.0)
├[6/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[7/25]┐ {1}(⟨G⟩(minimum⁻[A] < 1.019561))
│      ├✔ 4 : (ninstances = 58, ncovered = 58, confidence = 0.14, lift = 1.0)
│      └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[8/25]┐ {1}(⟨G⟩(minimum⁺[B] ≥ -0.343207))
│      ├✔ 3 : (ninstances = 48, ncovered = 48, confidence = 0.46, lift = 1.0)
│      └✘ 2 : (ninstances = 32, ncovered = 32, confidence = 0.78, lift = 1.0)
├[9/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.434608))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│      └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[10/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[11/25]┐ {1}(⟨G⟩(minimum⁺[J] ≥ -0.144559))
│       ├✔ 4 : (ninstances = 39, ncovered = 39, confidence = 0.15, lift = 1.0)
│       └✘ 2 : (ninstances = 41, ncovered = 41, confidence = 0.54, lift = 1.0)
├[12/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[13/25]┐ {1}(⟨G⟩(minimum⁻[I] < -0.243571))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[14/25]┐ {1}(⟨G⟩(minimum⁻[F] < -0.575495))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
├[15/25]┐ {1}(⟨G⟩(minimum⁻[D] < -0.568272))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.86, lift = 1.0)
├[16/25]┐ {1}(⟨G⟩(minimum⁺[D] ≥ -0.446165))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.24, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[17/25]┐ {1}(⟨G⟩(minimum⁻[A] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[18/25]┐ {1}(⟨G⟩(minimum⁺[I] ≥ 0.083275))
│       ├✔ 4 : (ninstances = 14, ncovered = 14, confidence = 0.43, lift = 1.0)
│       └✘ 2 : (ninstances = 66, ncovered = 66, confidence = 0.38, lift = 1.0)
├[19/25]┐ {1}(⟨G⟩(minimum⁻[A] < 1.019561))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[20/25]┐ {1}(⟨G⟩(minimum⁺[D] ≥ -0.4391))
│       ├✔ 1 : (ninstances = 44, ncovered = 44, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 36, ncovered = 36, confidence = 0.22, lift = 1.0)
├[21/25]┐ {1}(⟨G⟩(minimum⁻[B] < -0.621772))
│       ├✔ 2 : (ninstances = 33, ncovered = 33, confidence = 0.76, lift = 1.0)
│       └✘ 3 : (ninstances = 47, ncovered = 47, confidence = 0.47, lift = 1.0)
├[22/25]┐ {1}(⟨G⟩(minimum⁺[A] ≥ 1.320922))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[23/25]┐ {1}(⟨G⟩(minimum⁺[J] ≥ -0.068057))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.73, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
├[24/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.434608))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
└[25/25]┐ {1}(⟨G⟩(minimum⁺[C] ≥ 0.212466))
        ├✔ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
        └✘ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
▣ Ensemble{String} of 25 models of type DecisionTree{String}
├[1/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[2/25]┐ {1}(⟨G⟩(minimum⁺[V2] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[3/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[4/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 0.562459))
│      ├✔ 2 : (ninstances = 48, ncovered = 48, confidence = 0.5, lift = 1.0)
│      └✘ 4 : (ninstances = 32, ncovered = 32, confidence = 0.19, lift = 1.0)
├[5/25]┐ {1}(⟨G⟩(minimum⁺[V1] ≥ 1.320922))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│      └✘ 3 : (ninstances = 60, ncovered = 60, confidence = 0.42, lift = 1.0)
├[6/25]┐ {1}(⟨G⟩(minimum⁺[V2] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[7/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 1.019561))
│      ├✔ 4 : (ninstances = 58, ncovered = 58, confidence = 0.14, lift = 1.0)
│      └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[8/25]┐ {1}(⟨G⟩(minimum⁺[V2] ≥ -0.343207))
│      ├✔ 3 : (ninstances = 48, ncovered = 48, confidence = 0.46, lift = 1.0)
│      └✘ 2 : (ninstances = 32, ncovered = 32, confidence = 0.78, lift = 1.0)
├[9/25]┐ {1}(⟨G⟩(minimum⁺[V3] ≥ 0.434608))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│      └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[10/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[11/25]┐ {1}(⟨G⟩(minimum⁺[V10] ≥ -0.144559))
│       ├✔ 4 : (ninstances = 39, ncovered = 39, confidence = 0.15, lift = 1.0)
│       └✘ 2 : (ninstances = 41, ncovered = 41, confidence = 0.54, lift = 1.0)
├[12/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[13/25]┐ {1}(⟨G⟩(minimum⁻[V9] < -0.243571))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[14/25]┐ {1}(⟨G⟩(minimum⁻[V6] < -0.575495))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
├[15/25]┐ {1}(⟨G⟩(minimum⁻[V4] < -0.568272))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.86, lift = 1.0)
├[16/25]┐ {1}(⟨G⟩(minimum⁺[V4] ≥ -0.446165))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.24, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[17/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[18/25]┐ {1}(⟨G⟩(minimum⁺[V9] ≥ 0.083275))
│       ├✔ 4 : (ninstances = 14, ncovered = 14, confidence = 0.43, lift = 1.0)
│       └✘ 2 : (ninstances = 66, ncovered = 66, confidence = 0.38, lift = 1.0)
├[19/25]┐ {1}(⟨G⟩(minimum⁻[V1] < 1.019561))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[20/25]┐ {1}(⟨G⟩(minimum⁺[V4] ≥ -0.4391))
│       ├✔ 1 : (ninstances = 44, ncovered = 44, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 36, ncovered = 36, confidence = 0.22, lift = 1.0)
├[21/25]┐ {1}(⟨G⟩(minimum⁻[V2] < -0.621772))
│       ├✔ 2 : (ninstances = 33, ncovered = 33, confidence = 0.76, lift = 1.0)
│       └✘ 3 : (ninstances = 47, ncovered = 47, confidence = 0.47, lift = 1.0)
├[22/25]┐ {1}(⟨G⟩(minimum⁺[V1] ≥ 1.320922))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[23/25]┐ {1}(⟨G⟩(minimum⁺[V10] ≥ -0.068057))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.73, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
├[24/25]┐ {1}(⟨G⟩(minimum⁺[V3] ≥ 0.434608))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
└[25/25]┐ {1}(⟨G⟩(minimum⁺[V3] ≥ 0.212466))
        ├✔ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
        └✘ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
▣ Ensemble{String} of 25 models of type DecisionTree{String}
├[1/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[2/25]┐ (⟨G⟩(minimum⁺[coefficient2] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[3/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 0.91504))
│      ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│      └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[4/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 0.562459))
│      ├✔ 2 : (ninstances = 48, ncovered = 48, confidence = 0.5, lift = 1.0)
│      └✘ 4 : (ninstances = 32, ncovered = 32, confidence = 0.19, lift = 1.0)
├[5/25]┐ (⟨G⟩(minimum⁺[coefficient1] ≥ 1.320922))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│      └✘ 3 : (ninstances = 60, ncovered = 60, confidence = 0.42, lift = 1.0)
├[6/25]┐ (⟨G⟩(minimum⁺[coefficient2] ≥ -0.408784))
│      ├✔ 4 : (ninstances = 51, ncovered = 51, confidence = 0.14, lift = 1.0)
│      └✘ 2 : (ninstances = 29, ncovered = 29, confidence = 0.86, lift = 1.0)
├[7/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 1.019561))
│      ├✔ 4 : (ninstances = 58, ncovered = 58, confidence = 0.14, lift = 1.0)
│      └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[8/25]┐ (⟨G⟩(minimum⁺[coefficient2] ≥ -0.343207))
│      ├✔ 3 : (ninstances = 48, ncovered = 48, confidence = 0.46, lift = 1.0)
│      └✘ 2 : (ninstances = 32, ncovered = 32, confidence = 0.78, lift = 1.0)
├[9/25]┐ (⟨G⟩(minimum⁺[coefficient3] ≥ 0.434608))
│      ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│      └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[10/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[11/25]┐ (⟨G⟩(minimum⁺[coefficient10] ≥ -0.144559))
│       ├✔ 4 : (ninstances = 39, ncovered = 39, confidence = 0.15, lift = 1.0)
│       └✘ 2 : (ninstances = 41, ncovered = 41, confidence = 0.54, lift = 1.0)
├[12/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[13/25]┐ (⟨G⟩(minimum⁻[coefficient9] < -0.243571))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[14/25]┐ (⟨G⟩(minimum⁻[coefficient6] < -0.575495))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
├[15/25]┐ (⟨G⟩(minimum⁻[coefficient4] < -0.568272))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.86, lift = 1.0)
├[16/25]┐ (⟨G⟩(minimum⁺[coefficient4] ≥ -0.446165))
│       ├✔ 2 : (ninstances = 46, ncovered = 46, confidence = 0.24, lift = 1.0)
│       └✘ 4 : (ninstances = 34, ncovered = 34, confidence = 0.24, lift = 1.0)
├[17/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 0.91504))
│       ├✔ 3 : (ninstances = 56, ncovered = 56, confidence = 0.45, lift = 1.0)
│       └✘ 1 : (ninstances = 24, ncovered = 24, confidence = 0.92, lift = 1.0)
├[18/25]┐ (⟨G⟩(minimum⁺[coefficient9] ≥ 0.083275))
│       ├✔ 4 : (ninstances = 14, ncovered = 14, confidence = 0.43, lift = 1.0)
│       └✘ 2 : (ninstances = 66, ncovered = 66, confidence = 0.38, lift = 1.0)
├[19/25]┐ (⟨G⟩(minimum⁻[coefficient1] < 1.019561))
│       ├✔ 3 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
│       └✘ 1 : (ninstances = 22, ncovered = 22, confidence = 0.95, lift = 1.0)
├[20/25]┐ (⟨G⟩(minimum⁺[coefficient4] ≥ -0.4391))
│       ├✔ 1 : (ninstances = 44, ncovered = 44, confidence = 0.48, lift = 1.0)
│       └✘ 4 : (ninstances = 36, ncovered = 36, confidence = 0.22, lift = 1.0)
├[21/25]┐ (⟨G⟩(minimum⁻[coefficient2] < -0.621772))
│       ├✔ 2 : (ninstances = 33, ncovered = 33, confidence = 0.76, lift = 1.0)
│       └✘ 3 : (ninstances = 47, ncovered = 47, confidence = 0.47, lift = 1.0)
├[22/25]┐ (⟨G⟩(minimum⁺[coefficient1] ≥ 1.320922))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 1.0, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
├[23/25]┐ (⟨G⟩(minimum⁺[coefficient10] ≥ -0.068057))
│       ├✔ 3 : (ninstances = 22, ncovered = 22, confidence = 0.73, lift = 1.0)
│       └✘ 2 : (ninstances = 58, ncovered = 58, confidence = 0.41, lift = 1.0)
├[24/25]┐ (⟨G⟩(minimum⁺[coefficient3] ≥ 0.434608))
│       ├✔ 1 : (ninstances = 20, ncovered = 20, confidence = 0.95, lift = 1.0)
│       └✘ 4 : (ninstances = 60, ncovered = 60, confidence = 0.13, lift = 1.0)
└[25/25]┐ (⟨G⟩(minimum⁺[coefficient3] ≥ 0.212466))
        ├✔ 2 : (ninstances = 58, ncovered = 58, confidence = 0.43, lift = 1.0)
        └✘ 3 : (ninstances = 22, ncovered = 22, confidence = 0.91, lift = 1.0)
[ Info: Not retraining machine(ModalAdaBoost(max_depth = 1, …), …). Use `force=true` to force.
  0.119316 seconds (29.24 k allocations: 1.480 MiB, 99.01% compilation time)
==================================================
TEST: classification/digits.jl
[ Info: Precomputing logiset...

Computing logiset...   0%|                               |  ETA: 0:21:05
Computing logiset... 100%|███████████████████████████████| Time: 0:00:01
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
332.357191 seconds (148.97 M allocations: 8.342 GiB, 1.20% gc time, 98.07% compilation time)
┌ Warning: n_subfeatures must be > 0, but 0 was provided. Defaulting to nothing.
└ @ ModalDecisionTrees.MLJInterface ~/.julia/packages/ModalDecisionTrees/WJRxy/src/interfaces/MLJ/ModalDecisionTree.jl:120
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = 6, …), …).
 13.574116 seconds (22.93 M allocations: 2.046 GiB, 9.81% gc time, 59.78% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalRandomForest(sampling_fraction = 0.7, …), …).
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
  4.897036 seconds (21.97 M allocations: 2.010 GiB, 10.74% gc time, 0.48% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
139.252222 seconds (46.56 M allocations: 2.926 GiB, 0.63% gc time, 97.79% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
108.907801 seconds (78.26 M allocations: 3.046 GiB, 2.08% gc time, 92.12% compilation time)
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = nothing, …), …).
  5.492210 seconds (21.97 M allocations: 2.010 GiB, 26.63% gc time, 0.43% compilation time)
┌ Warning: n_subfeatures must be > 0, but 0 was provided. Defaulting to nothing.
└ @ ModalDecisionTrees.MLJInterface ~/.julia/packages/ModalDecisionTrees/WJRxy/src/interfaces/MLJ/ModalDecisionTree.jl:120
[ Info: Precomputing logiset...
[ Info: Training machine(ModalDecisionTree(max_depth = 6, …), …).
  5.149600 seconds (20.84 M allocations: 1.937 GiB, 26.37% gc time, 0.45% compilation time)
┌ Warning: Assignment to `mach` in soft scope is ambiguous because a global variable by the same name exists: `mach` will be treated as a new local. Disambiguate by using `local mach` to suppress this warning or `global mach` to assign to the existing global variable.
└ @ ~/.julia/packages/ModalDecisionTrees/WJRxy/test/classification/digits.jl:111
┌ Warning: An absolute n_subfeatures was provided 3. It is recommended to use relative values (between 0 and 1), interpreted as the share of the random portion of feature space explored at each split.
└ @ ModalDecisionTrees.MLJInterface ~/.julia/packages/ModalDecisionTrees/WJRxy/src/interfaces/MLJ/ModalRandomForest.jl:125
[ Info: Precomputing logiset...
[ Info: Training machine(ModalRandomForest(sampling_fraction = 0.7, …), …).
┌ Error: Problem fitting the machine machine(ModalRandomForest(sampling_fraction = 0.7, …), …). 
└ @ MLJBase ~/.julia/packages/MLJBase/GY2fM/src/machines.jl:695
[ Info: Running type checks... 
[ Info: Type checks okay. 
[ Info: Precomputing logiset...
[ Info: Training machine(ModalRandomForest(sampling_fraction = 0.7, …), …).

======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile
======================================================================================

cmd: /opt/julia/bin/julia 135 running 1 of 1

signal (10): User defined signal 1
_ZN12_GLOBAL__N_18Verifier19verifyFunctionAttrsEPN4llvm12FunctionTypeENS1_13AttributeListEPKNS1_5ValueEbb at /opt/julia/bin/../lib/julia/libLLVM-16jl.so (unknown line)
unknown function (ip: 0x57a264af)
unknown function (ip: (nil))

==============================================================
Profile collected. A report will print at the next yield point
==============================================================


======================================================================================
Information request received. A stacktrace will print followed by a 1.0 second profile
======================================================================================

cmd: /opt/julia/bin/julia 1 running 0 of 1

signal (10): User defined signal 1
epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404
uv_run at /workspace/srcdir/libuv/src/unix/core.c:430
ijl_task_get_next at /source/src/scheduler.c:522
poptask at ./task.jl:1012
wait at ./task.jl:1021
#wait#733 at ./condition.jl:130
wait at ./condition.jl:125 [inlined]
wait at ./process.jl:694
wait at ./process.jl:687
unknown function (ip: 0x72763e967b32)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
subprocess_handler at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2146
#131 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2086
withenv at ./env.jl:265
#118 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1935
with_temp_env at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1793
#116 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1902
#mktempdir#28 at ./file.jl:819
unknown function (ip: 0x72763e95e26d)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
mktempdir at ./file.jl:815
mktempdir at ./file.jl:815 [inlined]
#sandbox#115 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1849 [inlined]
sandbox at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:1841
unknown function (ip: 0x72763e9527ea)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
#test#128 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2067
test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/Operations.jl:2011 [inlined]
#test#146 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:481
test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:460
unknown function (ip: 0x72763e9524ed)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
#test#77 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:159
unknown function (ip: 0x72763e951ecd)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:148
#test#75 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:147 [inlined]
test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:147 [inlined]
#test#74 at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:146 [inlined]
test at /source/usr/share/julia/stdlib/v1.11/Pkg/src/API.jl:146
unknown function (ip: 0x72763e94e366)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
jl_apply at /source/src/julia.h:2157 [inlined]
do_call at /source/src/interpreter.c:126
eval_value at /source/src/interpreter.c:223
eval_stmt_value at /source/src/interpreter.c:174 [inlined]
eval_body at /source/src/interpreter.c:670
eval_body at /source/src/interpreter.c:539
eval_body at /source/src/interpreter.c:539
jl_interpret_toplevel_thunk at /source/src/interpreter.c:824
jl_toplevel_eval_flex at /source/src/toplevel.c:943
jl_toplevel_eval_flex at /source/src/toplevel.c:886
ijl_toplevel_eval_in at /source/src/toplevel.c:994
eval at ./boot.jl:430 [inlined]
include_string at ./loading.jl:2775
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
_include at ./loading.jl:2835
include at ./Base.jl:562
jfptr_include_47022.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
exec_options at ./client.jl:316
_start at ./client.jl:524
jfptr__start_73678.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
jl_apply at /source/src/julia.h:2157 [inlined]
true_main at /source/src/jlapi.c:900
jl_repl_entrypoint at /source/src/jlapi.c:1059
main at /source/cli/loader_exe.c:58
unknown function (ip: 0x72763ff3e249)
__libc_start_main at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
unknown function (ip: 0x4010b8)
unknown function (ip: (nil))

==============================================================
Profile collected. A report will print at the next yield point
==============================================================

┌ Warning: There were no samples collected in one or more groups.
│ This may be due to idle threads, or you may need to run your
│ program longer (perhaps by running it multiple times),
│ or adjust the delay between samples with `Profile.init()`.
└ @ Profile /opt/julia/share/julia/stdlib/v1.11/Profile/src/Profile.jl:1240
Overhead ╎ [+additional indent] Count File:Line; Function
=========================================================
Thread 1 Task 0x0000727632ffc010 Total snapshots: 60. Utilization: 0%
  ╎60 @Base/client.jl:524; _start()
  ╎ 60 @Base/client.jl:316; exec_options(opts::Base.JLOptions)
  ╎  60 @Base/Base.jl:562; include(mod::Module, _path::String)
  ╎   60 @Base/loading.jl:2835; _include(mapexpr::Function, mod::Module, _path:…
  ╎    60 @Base/loading.jl:2775; include_string(mapexpr::typeof(identity), mod:…
  ╎     60 @Base/boot.jl:430; eval
  ╎    ╎ 60 @Pkg/src/API.jl:146; kwcall(::@NamedTuple{julia_args::Cmd}, ::typeo…
  ╎    ╎  60 @Pkg/src/API.jl:146; #test#74
  ╎    ╎   60 @Pkg/src/API.jl:147; test
  ╎    ╎    60 @Pkg/src/API.jl:147; #test#75
  ╎    ╎     60 @Pkg/src/API.jl:148; kwcall(::@NamedTuple{julia_args::Cmd}, ::t…
  ╎    ╎    ╎ 60 @Pkg/src/API.jl:159; test(pkgs::Vector{Pkg.Types.PackageSpec};…
  ╎    ╎    ╎  60 @Pkg/src/API.jl:460; kwcall(::@NamedTuple{julia_args::Cmd, io…
  ╎    ╎    ╎   60 @Pkg/src/API.jl:481; test(ctx::Pkg.Types.Context, pkgs::Vect…
  ╎    ╎    ╎    60 …src/Operations.jl:2011; test
  ╎    ╎    ╎     60 …src/Operations.jl:2067; test(ctx::Pkg.Types.Context, pkgs…
  ╎    ╎    ╎    ╎ 60 …rc/Operations.jl:1841; kwcall(::@NamedTuple{preferences:…
  ╎    ╎    ╎    ╎  60 …rc/Operations.jl:1849; #sandbox#115
  ╎    ╎    ╎    ╎   60 @Base/file.jl:815; mktempdir
  ╎    ╎    ╎    ╎    60 @Base/file.jl:815; mktempdir(fn::Function, parent::Str…
  ╎    ╎    ╎    ╎     60 @Base/file.jl:819; mktempdir(fn::Pkg.Operations.var"#…
  ╎    ╎    ╎    ╎    ╎ 60 …/Operations.jl:1902; (::Pkg.Operations.var"#116#121…
  ╎    ╎    ╎    ╎    ╎  60 …/Operations.jl:1793; with_temp_env(fn::Pkg.Operati…
  ╎    ╎    ╎    ╎    ╎   60 …Operations.jl:1935; (::Pkg.Operations.var"#118#12…
  ╎    ╎    ╎    ╎    ╎    60 @Base/env.jl:265; withenv(::Pkg.Operations.var"#1…
  ╎    ╎    ╎    ╎    ╎     60 …Operations.jl:2086; (::Pkg.Operations.var"#131#…
  ╎    ╎    ╎    ╎    ╎    ╎ 60 …perations.jl:2146; subprocess_handler(cmd::Cmd…
  ╎    ╎    ╎    ╎    ╎    ╎  60 …e/process.jl:687; wait(x::Base.Process)
  ╎    ╎    ╎    ╎    ╎    ╎   60 …e/process.jl:694; wait(x::Base.Process, sync…
  ╎    ╎    ╎    ╎    ╎    ╎    60 …ondition.jl:125; wait
  ╎    ╎    ╎    ╎    ╎    ╎     60 …ondition.jl:130; wait(c::Base.GenericCondi…
  ╎    ╎    ╎    ╎    ╎    ╎    ╎ 60 …ase/task.jl:1021; wait()
59╎    ╎    ╎    ╎    ╎    ╎    ╎  60 …ase/task.jl:1012; poptask(W::Base.Intrus…

┌ Warning: There were no samples collected in one or more groups.
│ This may be due to idle threads, or you may need to run your
│ program longer (perhaps by running it multiple times),
│ or adjust the delay between samples with `Profile.init()`.
└ @ Profile /opt/julia/share/julia/stdlib/v1.11/Profile/src/Profile.jl:1240
Overhead ╎ [+additional indent] Count File:Line; Function
=========================================================
Thread 1 Task 0x00007f30b61f7850 Total snapshots: 30. Utilization: 100%
  ╎26 …nTrees/src/ModalCART.jl:1092; (::ModalDecisionTrees.var"#79#86"{ModalDec…
  ╎ 26 …Trees/src/ModalCART.jl:1067; (::ModalDecisionTrees.var"#process_node!#8…
  ╎  26 …Trees/src/ModalCART.jl:317; kwcall(::@NamedTuple{idxs::Vector{Int64}, …
  ╎   26 …Trees/src/ModalCART.jl:965; #optimize_node!#72
  ╎    26 …rees/src/ModalCART.jl:560; (::ModalDecisionTrees.var"#splitnode!#73"…
  ╎     26 …-onestep-decisions.jl:37; modalstep(X::SupportedLogiset{Interval2D{…
  ╎    ╎ 26 …-onestep-decisions.jl:77; modalstep(X::SupportedLogiset{Interval2D…
  ╎    ╎  26 @Base/generator.jl:45; iterate
  ╎    ╎   26 @Base/iterators.jl:427; iterate
  ╎    ╎    26 @Base/iterators.jl:436; _zip_iterate_all
25╎    ╎     26 @Base/iterators.jl:444; _zip_iterate_some


[1] signal 15: Terminated
in expression starting at /PkgEval.jl/scripts/evaluate.jl:210
epoll_pwait at /lib/x86_64-linux-gnu/libc.so.6 (unknown line)
uv__io_poll at /workspace/srcdir/libuv/src/unix/linux.c:1404
uv_run at /workspace/srcdir/libuv/src/unix/core.c:430
ijl_task_get_next at /source/src/scheduler.c:522
poptask at ./task.jl:1012
wait at ./task.jl:1021
#wait#733 at ./condition.jl:130
wait at ./condition.jl:125 [inlined]
_trywait at ./asyncevent.jl:145
profile_printing_listener at ./Base.jl:582
#1192 at ./Base.jl:622
jfptr_YY.1192_76252.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
jl_apply at /source/src/julia.h:2157 [inlined]
start_task at /source/src/task.c:1202
unknown function (ip: (nil))
Allocations: 23274585 (Pool: 23272839; Big: 1746); GC: 25
val already in a list
atexit hook threw an error: ErrorException("schedule: Task not runnable")

[135] signal 15: Terminated
in expression starting at /home/pkgeval/.julia/packages/ModalDecisionTrees/WJRxy/test/classification/digits.jl:122
error at ./error.jl:35
#schedule#763 at ./task.jl:884
schedule at ./task.jl:876 [inlined]
uv_writecb_task at ./stream.jl:1200
getindex at ./essentials.jl:917 [inlined]
_useref_getindex at ./compiler/ssair/ir.jl:464
jfptr_uv_writecb_task_67129.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
getindex at ./compiler/ssair/ir.jl:499 [inlined]
scan_inconsistency! at ./compiler/optimize.jl:825
jlcapi_uv_writecb_task_67593.1 at /opt/julia/lib/julia/sys.so (unknown line)
uv__write_callbacks at /workspace/srcdir/libuv/src/unix/stream.c:926
uv__stream_io at /workspace/srcdir/libuv/src/unix/stream.c:1227
uv__run_pending at /workspace/srcdir/libuv/src/unix/core.c:824
uv_run at /workspace/srcdir/libuv/src/unix/core.c:420
ijl_task_get_next at /source/src/scheduler.c:522
ScanStmt at ./compiler/optimize.jl:851
poptask at ./task.jl:1012
scan! at ./compiler/ssair/irinterp.jl:248
wait at ./task.jl:1021
uv_write at ./stream.jl:1081
ipo_dataflow_analysis! at ./compiler/optimize.jl:960
unsafe_write at ./stream.jl:1154
optimize at ./compiler/optimize.jl:984
write at ./strings/io.jl:248 [inlined]
print at ./strings/io.jl:250
jfptr_print_48479.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
showerror at ./errorshow.jl:152
unknown function (ip: 0x72763e974c76)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
_atexit at ./initdefs.jl:454
jfptr__atexit_69637.1 at /opt/julia/lib/julia/sys.so (unknown line)
_jl_invoke at /source/src/gf.c:2951 [inlined]
ijl_apply_generic at /source/src/gf.c:3128
jl_apply at /source/src/julia.h:2157 [inlined]
ijl_atexit_hook at /source/src/init.c:271
jl_exit_thread0_cb at /source/src/signals-unix.c:517

PkgEval terminated after 2726.35s: test duration exceeded the time limit