Package evaluation of EvoTrees on Julia 1.10.8 (92f03a4775*) started at 2025-02-25T12:36:22.181


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# Set-up
#

Installing PkgEval dependencies (TestEnv)...

Set-up completed after 5.01s


################################################################################
# Installation
#

Installing EvoTrees...
   Resolving package versions...
    Updating `~/.julia/environments/v1.10/Project.toml`
  [f6006082] + EvoTrees v0.16.9
    Updating `~/.julia/environments/v1.10/Manifest.toml`
  [66dad0bd] + AliasTables v1.1.3
  [fbb218c0] + BSON v0.3.9
  [324d7699] + CategoricalArrays v0.10.8
  [34da2185] + Compat v4.16.0
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.18.20
  [e2d170a0] + DataValueInterfaces v1.0.0
  [31c24e10] + Distributions v0.25.117
  [ffbed154] + DocStringExtensions v0.9.3
  [f6006082] + EvoTrees v0.16.9
  [411431e0] + Extents v0.1.5
  [1a297f60] + FillArrays v1.13.0
  [68eda718] + GeoFormatTypes v0.4.4
  [cf35fbd7] + GeoInterface v1.4.1
  [5c1252a2] + GeometryBasics v0.5.5
  [34004b35] + HypergeometricFunctions v0.3.27
  [92d709cd] + IrrationalConstants v0.2.4
  [c8e1da08] + IterTools v1.10.0
  [82899510] + IteratorInterfaceExtensions v1.0.0
  [692b3bcd] + JLLWrappers v1.7.0
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e80e1ace] + MLJModelInterface v1.11.0
  [e1d29d7a] + Missings v1.2.0
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  [bac558e1] + OrderedCollections v1.8.0
  [90014a1f] + PDMats v0.11.32
  [aea7be01] + PrecompileTools v1.2.1
  [21216c6a] + Preferences v1.4.3
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  [1fd47b50] + QuadGK v2.11.2
  [3cdcf5f2] + RecipesBase v1.3.4
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  [b77e0a4c] + InteractiveUtils
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  [76f85450] + LibGit2
  [8f399da3] + Libdl
  [37e2e46d] + LinearAlgebra
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  [e66e0078] + CompilerSupportLibraries_jll v1.1.1+0
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  [4536629a] + OpenBLAS_jll v0.3.23+4
  [05823500] + OpenLibm_jll v0.8.1+4
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  [83775a58] + Zlib_jll v1.2.13+1
  [8e850b90] + libblastrampoline_jll v5.11.0+0
  [8e850ede] + nghttp2_jll v1.52.0+1
  [3f19e933] + p7zip_jll v17.4.0+2

Installation completed after 8.1s


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# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...

Precompilation completed after 127.68s


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# Testing
#

     Testing EvoTrees
      Status `/tmp/jl_jSgx9z/Project.toml`
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        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.1285054236650467
┌ Info: iter 25
└   metric = 0.021750634536147118
┌ Info: iter 50
└   metric = 0.020412476733326912
┌ Info: iter 75
└   metric = 0.020412830635905266
┌ Info: iter 100
└   metric = 0.020485317334532738
┌ Info: EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.6703526377677917
┌ Info: iter 25
└   metric = 0.47962743043899536
┌ Info: iter 50
└   metric = 0.4349091053009033
┌ Info: iter 75
└   metric = 0.42034342885017395
┌ Info: iter 100
└   metric = 0.41411060094833374
┌ Info: EvoTreeRegressor{EvoTrees.Gamma}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 1.4709550142288208
┌ Info: iter 25
└   metric = 0.32610952854156494
┌ Info: iter 50
└   metric = 0.30085092782974243
┌ Info: iter 75
└   metric = 0.2962430417537689
┌ Info: iter 100
└   metric = 0.29673075675964355
┌ Info: EvoTreeRegressor{EvoTrees.Tweedie}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.5471090078353882
┌ Info: iter 25
└   metric = 0.1559978425502777
┌ Info: iter 50
└   metric = 0.11321545392274857
┌ Info: iter 75
└   metric = 0.10234956443309784
┌ Info: iter 100
└   metric = 0.100061796605587
┌ Info: EvoTreeRegressor{EvoTrees.L1}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.32020843029022217
┌ Info: iter 25
└   metric = 0.20671051740646362
┌ Info: iter 50
└   metric = 0.1563587784767151
┌ Info: iter 75
└   metric = 0.129248708486557
┌ Info: iter 100
└   metric = 0.11566969007253647
┌ Info: EvoTreeRegressor{EvoTrees.Quantile}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.16010421514511108
┌ Info: iter 25
└   metric = 0.06665664911270142
┌ Info: iter 50
└   metric = 0.051511265337467194
┌ Info: iter 75
└   metric = 0.049108609557151794
┌ Info: iter 100
└   metric = 0.049054332077503204
[ Info: The following kwargs are not supported and will be ignored: [:loss].
┌ Info: EvoTreeCount{EvoTrees.Poisson}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.29576486349105835
┌ Info: iter 25
└   metric = 0.09861209988594055
┌ Info: iter 50
└   metric = 0.07009350508451462
┌ Info: iter 75
└   metric = 0.06052471697330475
┌ Info: iter 100
└   metric = 0.056582193821668625
┌ Info: EvoTreeMLE{EvoTrees.GaussianMLE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 10.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.5254025459289551
┌ Info: iter 25
└   metric = 1.6796438694000244
┌ Info: iter 50
└   metric = 1.7593154907226562
┌ Info: iter 75
└   metric = 1.5544557571411133
┌ Info: iter 100
└   metric = 1.5220974683761597
┌ Info: EvoTreeMLE{EvoTrees.LogisticMLE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 10.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = -1.7957830429077148
┌ Info: iter 25
└   metric = -0.0885191559791565
┌ Info: iter 50
└   metric = -0.2151053249835968
┌ Info: iter 75
└   metric = -0.328294575214386
┌ Info: iter 100
└   metric = -0.3560096323490143
┌ Info: EvoTreeGaussian{EvoTrees.GaussianMLE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 10.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: initialization
└   metric = 0.5254025459289551
┌ Info: iter 25
└   metric = 1.6796438694000244
┌ Info: iter 50
└   metric = 1.7593154907226562
┌ Info: iter 75
└   metric = 1.5544557571411133
┌ Info: iter 100
└   metric = 1.5220974683761597
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.3
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
└  - rng: MersenneTwister(1994638984)
┌ Info: EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.3
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
└  - rng: MersenneTwister(1994638984)
┌ Info: EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.3
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
└  - rng: MersenneTwister(1994638984)
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.13038957118988037
┌ Info: iter 25
└   metric = 0.012286867015063763
┌ Info: iter 50
└   metric = 0.004691367968916893
┌ Info: iter 75
└   metric = 0.002263386268168688
┌ Info: iter 100
└   metric = 0.0013307810295373201
┌ Info: iter 125
└   metric = 0.0009321333491243422
┌ Info: iter 150
└   metric = 0.0007339680450968444
┌ Info: iter 175
└   metric = 0.000638530240394175
┌ Info: iter 200
└   metric = 0.0005851167952641845
┌ Info: EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.6824027299880981
┌ Info: iter 25
└   metric = 0.41121602058410645
┌ Info: iter 50
└   metric = 0.383105993270874
┌ Info: iter 75
└   metric = 0.37144172191619873
┌ Info: iter 100
└   metric = 0.36528101563453674
┌ Info: iter 125
└   metric = 0.3618107736110687
┌ Info: iter 150
└   metric = 0.3594982922077179
┌ Info: iter 175
└   metric = 0.35791245102882385
┌ Info: iter 200
└   metric = 0.3567737638950348
┌ Info: EvoTreeRegressor{EvoTrees.Quantile}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.16202571988105774
┌ Info: iter 25
└   metric = 0.04000483453273773
┌ Info: iter 50
└   metric = 0.024839557707309723
┌ Info: iter 75
└   metric = 0.018065698444843292
┌ Info: iter 100
└   metric = 0.01421225443482399
┌ Info: iter 125
└   metric = 0.012194428592920303
┌ Info: iter 150
└   metric = 0.010665226727724075
┌ Info: iter 175
└   metric = 0.009889934211969376
┌ Info: iter 200
└   metric = 0.009328466840088367
┌ Info: EvoTreeRegressor{EvoTrees.L1}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.3240514397621155
┌ Info: iter 25
└   metric = 0.13767774403095245
┌ Info: iter 50
└   metric = 0.08941688388586044
┌ Info: iter 75
└   metric = 0.06840011477470398
┌ Info: iter 100
└   metric = 0.05573507770895958
┌ Info: iter 125
└   metric = 0.045757342129945755
┌ Info: iter 150
└   metric = 0.039157334715127945
┌ Info: iter 175
└   metric = 0.03408438712358475
┌ Info: iter 200
└   metric = 0.0302757378667593
┌ Info: EvoTreeRegressor{EvoTrees.Gamma}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 1.35107421875
┌ Info: iter 25
└   metric = 13.538475036621094
┌ Info: iter 50
└   metric = 1.44353449344635
┌ Info: iter 75
└   metric = 0.6693938970565796
┌ Info: iter 100
└   metric = 0.4668451249599457
┌ Info: iter 125
└   metric = 0.3467889130115509
┌ Info: iter 150
└   metric = 0.27195560932159424
┌ Info: iter 175
└   metric = 0.22249466180801392
┌ Info: iter 200
└   metric = 0.19097940623760223
┌ Info: EvoTreeRegressor{EvoTrees.Tweedie}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.5394082069396973
┌ Info: iter 25
└   metric = 0.058856260031461716
┌ Info: iter 50
└   metric = 0.023161834105849266
┌ Info: iter 75
└   metric = 0.01451699249446392
┌ Info: iter 100
└   metric = 0.011430804617702961
┌ Info: iter 125
└   metric = 0.009545471519231796
┌ Info: iter 150
└   metric = 0.008359222672879696
┌ Info: iter 175
└   metric = 0.007589627988636494
┌ Info: iter 200
└   metric = 0.007102351635694504
┌ Info: EvoTreeCount{EvoTrees.Poisson}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.29837360978126526
┌ Info: iter 25
└   metric = 0.037789445370435715
┌ Info: iter 50
└   metric = 0.014368334785103798
┌ Info: iter 75
└   metric = 0.0069135804660618305
┌ Info: iter 100
└   metric = 0.004017531871795654
┌ Info: iter 125
└   metric = 0.002805836731567979
┌ Info: iter 150
└   metric = 0.0022382643073797226
┌ Info: iter 175
└   metric = 0.0019868973176926374
┌ Info: iter 200
└   metric = 0.001820261008106172
┌ Info: EvoTreeMLE{EvoTrees.GaussianMLE}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 8.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.5180503726005554
┌ Info: iter 25
└   metric = 1.0875798463821411
┌ Info: iter 50
└   metric = 1.464343547821045
┌ Info: iter 75
└   metric = 1.733241319656372
┌ Info: iter 100
└   metric = 1.9205900430679321
┌ Info: iter 125
└   metric = 2.0139412879943848
┌ Info: iter 150
└   metric = 2.0636789798736572
┌ Info: iter 175
└   metric = 2.1103556156158447
┌ Info: iter 200
└   metric = 2.157722234725952
┌ Info: EvoTreeMLE{EvoTrees.LogisticMLE}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 8.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = -1.9062025547027588
┌ Info: iter 25
└   metric = -0.20996643602848053
┌ Info: iter 50
└   metric = 0.23715250194072723
┌ Info: iter 75
└   metric = 0.3656861186027527
┌ Info: iter 100
└   metric = 0.4394267201423645
┌ Info: iter 125
└   metric = 0.5046214461326599
┌ Info: iter 150
└   metric = 0.5554330945014954
┌ Info: iter 175
└   metric = 0.6037527322769165
┌ Info: iter 200
└   metric = 0.6462991237640381
┌ Info: EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - tree_type: oblivious
└  - rng: MersenneTwister(123, (0, 2004, 1002, 598))
┌ Info: initialization
└   metric = 0.6782814264297485
┌ Info: iter 25
└   metric = 0.19514018297195435
┌ Info: iter 50
└   metric = 0.12145228683948517
┌ Info: iter 75
└   metric = 0.09427497535943985
┌ Info: iter 100
└   metric = 0.0814461037516594
┌ Info: iter 125
└   metric = 0.074638232588768
┌ Info: iter 150
└   metric = 0.07058083266019821
┌ Info: iter 175
└   metric = 0.06783214956521988
┌ Info: iter 200
└   metric = 0.06609634310007095
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 1002, 0, 800))
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 15030, 14028, 272))
┌ Info: initialization
└   metric = 0.12692689895629883
┌ Info: iter 25
└   metric = 0.03485753387212753
┌ Info: iter 50
└   metric = 0.02188374474644661
┌ Info: iter 75
└   metric = 0.019843434914946556
┌ Info: iter 100
└   metric = 0.019498445093631744
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 29058, 28056, 544))
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 43086, 42084, 17))
┌ Info: initialization
└   metric = 0.12692689895629883
┌ Info: iter 25
└   metric = 0.035147134214639664
┌ Info: iter 50
└   metric = 0.022139327600598335
┌ Info: iter 75
└   metric = 0.019963815808296204
┌ Info: iter 100
└   metric = 0.01965385116636753
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 57114, 56112, 289))
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 71142, 70140, 761))
[ Info: The following kwargs are not supported and will be ignored: [:device].
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 0.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: initialization
└   metric = 0.02036040835082531
┌ Info: iter 25
└   metric = 0.004349107854068279
┌ Info: iter 50
└   metric = 0.0013789711520075798
┌ Info: iter 75
└   metric = 0.0008224270422942936
┌ Info: iter 100
└   metric = 0.0007158371736295521
┌ Info: iter 125
└   metric = 0.0006959998863749206
┌ Info: iter 150
└   metric = 0.0006921181920915842
┌ Info: iter 175
└   metric = 0.0006909396615810692
┌ Info: iter 200
└   metric = 0.0006905276677571237
[ Info: The following kwargs are not supported and will be ignored: [:device].
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.5
│  - max_depth: 6
│  - min_weight: 0.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict(1 => 1)
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: initialization
└   metric = 0.02036040835082531
┌ Info: iter 25
└   metric = 0.0024124239571392536
┌ Info: iter 50
└   metric = 0.0024110707454383373
┌ Info: iter 75
└   metric = 0.0024122465401887894
┌ Info: iter 100
└   metric = 0.00241235108114779
┌ Info: iter 125
└   metric = 0.0024120109155774117
┌ Info: iter 150
└   metric = 0.00241161254234612
┌ Info: iter 175
└   metric = 0.0024125627242028713
┌ Info: iter 200
└   metric = 0.0024116968270391226
[ Info: The following kwargs are not supported and will be ignored: [:device].
┌ Info: EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.05
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 0.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: initialization
└   metric = 0.4738384485244751
┌ Info: iter 25
└   metric = 0.3649347126483917
┌ Info: iter 50
└   metric = 0.33836615085601807
┌ Info: iter 75
└   metric = 0.33043116331100464
┌ Info: iter 100
└   metric = 0.3275209963321686
┌ Info: iter 125
└   metric = 0.32623955607414246
┌ Info: iter 150
└   metric = 0.32558995485305786
┌ Info: iter 175
└   metric = 0.32521945238113403
┌ Info: iter 200
└   metric = 0.3249880075454712
[ Info: The following kwargs are not supported and will be ignored: [:device].
┌ Info: EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 0.05
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 0.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict(1 => 1)
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: initialization
└   metric = 0.4738384485244751
┌ Info: iter 25
└   metric = 0.37152576446533203
┌ Info: iter 50
└   metric = 0.34896141290664673
┌ Info: iter 75
└   metric = 0.3434516489505768
┌ Info: iter 100
└   metric = 0.341949999332428
┌ Info: iter 125
└   metric = 0.3414995074272156
┌ Info: iter 150
└   metric = 0.3413655459880829
┌ Info: iter 175
└   metric = 0.34130987524986267
┌ Info: iter 200
└   metric = 0.3412889540195465
[ Info: The following kwargs are not supported and will be ignored: [:device, :metric].
┌ Info: EvoTreeGaussian{EvoTrees.GaussianMLE}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 6
│  - min_weight: 0.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: initialization
└   metric = 1.4467309713363647
┌ Info: iter 25
└   metric = 2.3835630416870117
┌ Info: iter 50
└   metric = 2.9986820220947266
┌ Info: iter 75
└   metric = 3.405792713165283
┌ Info: iter 100
└   metric = 3.644848585128784
┌ Info: iter 125
└   metric = 3.7724838256835938
┌ Info: iter 150
└   metric = 3.8453266620635986
┌ Info: iter 175
└   metric = 3.8830883502960205
┌ Info: iter 200
└   metric = 3.893998861312866
[ Info: The following kwargs are not supported and will be ignored: [:device, :metric].
┌ Info: EvoTreeGaussian{EvoTrees.GaussianMLE}
│  - nrounds: 200
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.5
│  - max_depth: 6
│  - min_weight: 0.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict(1 => 1)
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: initialization
└   metric = 1.4467309713363647
┌ Info: iter 25
└   metric = 2.730233907699585
┌ Info: iter 50
└   metric = 2.730408191680908
┌ Info: iter 75
└   metric = 2.73030948638916
┌ Info: iter 100
└   metric = 2.7303237915039062
┌ Info: iter 125
└   metric = 2.738657236099243
┌ Info: iter 150
└   metric = 2.738638401031494
┌ Info: iter 175
└   metric = 2.7388112545013428
┌ Info: iter 200
└   metric = 2.7389094829559326
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 3
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123)
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 3
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 14028, 13026, 674))
┌ Warning: To track eval metric in logger, both `metric` and `deval` must be provided.
└ @ EvoTrees ~/.julia/packages/EvoTrees/73o4j/src/fit.jl:380
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 3
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 28056, 27054, 146))
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 3
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 41082, 40080, 620))
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 3
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 41082, 40080, 620))
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 3
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 42084, 41082, 418))
┌ Info: EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.5
│  - gamma: 0.1
│  - eta: 0.05
│  - max_depth: 3
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 1.0
│  - nbins: 16
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
└  - rng: MersenneTwister(123, (0, 42084, 41082, 418))
┌ Info: Training machine(EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 10
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 5
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Updating machine(EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 20
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 5
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 2004, 1002, 908))
└ , …).
┌ Info: Training machine(EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 10
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 4
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Updating machine(EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 60
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.05
│  - max_depth: 4
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 1002, 0, 510))
└ , …).
[ Info: The following kwargs are not supported and will be ignored: [:loss, :metric].
┌ Info: Training machine(EvoTreeCount{EvoTrees.Poisson}
│  - nrounds: 10
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Updating machine(EvoTreeCount{EvoTrees.Poisson}
│  - nrounds: 20
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 3006, 2004, 106))
└ , …).
┌ Info: Training machine(EvoTreeGaussian{EvoTrees.GaussianMLE}
│  - nrounds: 10
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Updating machine(EvoTreeGaussian{EvoTrees.GaussianMLE}
│  - nrounds: 20
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 3006, 2004, 106))
└ , …).
┌ Info: Training machine(EvoTreeMLE{EvoTrees.LogisticMLE}
│  - nrounds: 10
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 32.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Updating machine(EvoTreeMLE{EvoTrees.LogisticMLE}
│  - nrounds: 20
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 32.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 3006, 2004, 106))
└ , …).
┌ Info: Training machine(EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Training machine(EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Training machine(EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 10
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 32.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Updating machine(EvoTreeRegressor{EvoTrees.LogLoss}
│  - nrounds: 20
│  - L2: 0.0
│  - lambda: 1.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 32.0
│  - rowsample: 0.5
│  - colsample: 0.5
│  - nbins: 32
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 3006, 2004, 106))
└ , …).
┌ Info: Training machine(EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Not retraining machine(EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 18036, 17034, 866))
└ , …). Use `force=true` to force.
┌ Info: Training machine(EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
┌ Info: Not retraining machine(EvoTreeRegressor{EvoTrees.MSE}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - monotone_constraints: Dict{Int64, Int64}()
│  - tree_type: binary
│  - rng: MersenneTwister(123, (0, 18036, 17034, 866))
└ , …). Use `force=true` to force.
┌ Info: Training machine(EvoTreeClassifier{EvoTrees.MLogLoss}
│  - nrounds: 100
│  - L2: 0.0
│  - lambda: 0.0
│  - gamma: 0.0
│  - eta: 0.1
│  - max_depth: 6
│  - min_weight: 1.0
│  - rowsample: 1.0
│  - colsample: 1.0
│  - nbins: 64
│  - alpha: 0.5
│  - tree_type: binary
│  - rng: MersenneTwister(123)
└ , …).
Test Summary: | Pass  Total     Time
EvoTrees      |  105    105  9m34.0s
     Testing EvoTrees tests passed 

Testing completed after 587.03s

PkgEval succeeded after 736.63s