Package evaluation to test SDeMo on Julia 1.14.0-DEV.1711 (41ad7d9eeb*) started at 2026-02-12T17:40:02.998


################################################################################
# Set-up
#

Installing PkgEval dependencies (TestEnv)...
  Activating project at `~/.julia/environments/v1.14`

Set-up completed after 12.09s


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

Installing SDeMo...
   Resolving package versions...
    Updating `~/.julia/environments/v1.14/Project.toml`
  [3e5feb82] + SDeMo v1.7.1
    Updating `~/.julia/environments/v1.14/Manifest.toml`
  [66dad0bd] + AliasTables v1.1.3
  [7d9fca2a] + Arpack v0.5.4
  [9a962f9c] + DataAPI v1.16.0
  [864edb3b] + DataStructures v0.19.3
  [31c24e10] + Distributions v0.25.123
  [ffbed154] + DocStringExtensions v0.9.5
  [1a297f60] + FillArrays v1.16.0
  [34004b35] + HypergeometricFunctions v0.3.28
  [92d709cd] + IrrationalConstants v0.2.6
  [692b3bcd] + JLLWrappers v1.7.1
  [682c06a0] + JSON v1.4.0
  [2ab3a3ac] + LogExpFunctions v0.3.29
  [e1d29d7a] + Missings v1.2.0
  [6f286f6a] + MultivariateStats v0.10.3
  [bac558e1] + OrderedCollections v1.8.1
  [90014a1f] + PDMats v0.11.37
  [69de0a69] + Parsers v2.8.3
  [aea7be01] + PrecompileTools v1.3.3
  [21216c6a] + Preferences v1.5.1
  [43287f4e] + PtrArrays v1.3.0
  [1fd47b50] + QuadGK v2.11.2
  [189a3867] + Reexport v1.2.2
  [79098fc4] + Rmath v0.9.0
  [3e5feb82] + SDeMo v1.7.1
  [a2af1166] + SortingAlgorithms v1.2.2
  [276daf66] + SpecialFunctions v2.7.1
  [10745b16] + Statistics v1.11.1
  [82ae8749] + StatsAPI v1.8.0
  [2913bbd2] + StatsBase v0.34.10
  [4c63d2b9] + StatsFuns v1.5.2
  [ec057cc2] + StructUtils v2.6.2
  [1c621080] + TestItems v1.0.0
⌅ [68821587] + Arpack_jll v3.5.2+0
  [efe28fd5] + OpenSpecFun_jll v0.5.6+0
  [f50d1b31] + Rmath_jll v0.5.1+0
  [56f22d72] + Artifacts v1.11.0
  [ade2ca70] + Dates v1.11.0
  [8f399da3] + Libdl v1.11.0
  [37e2e46d] + LinearAlgebra v1.13.0
  [56ddb016] + Logging v1.11.0
  [de0858da] + Printf v1.11.0
  [9a3f8284] + Random v1.11.0
  [ea8e919c] + SHA v1.0.0
  [9e88b42a] + Serialization v1.11.0
  [2f01184e] + SparseArrays v1.13.0
  [4607b0f0] + SuiteSparse
  [fa267f1f] + TOML v1.0.3
  [cf7118a7] + UUIDs v1.11.0
  [4ec0a83e] + Unicode v1.11.0
  [e66e0078] + CompilerSupportLibraries_jll v1.3.0+1
  [4536629a] + OpenBLAS_jll v0.3.30+0
  [05823500] + OpenLibm_jll v0.8.7+0
  [bea87d4a] + SuiteSparse_jll v7.10.1+0
  [8e850b90] + libblastrampoline_jll v5.15.0+0
        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 5.56s


################################################################################
# Precompilation
#

Precompiling PkgEval dependencies...

Precompiling package dependencies...
Precompiling packages...
   5339.8 ms  ✓ SDeMo
  1 dependency successfully precompiled in 6 seconds. 75 already precompiled.

Precompilation completed after 27.84s


################################################################################
# Testing
#

     Testing SDeMo
      Status `/tmp/jl_u5kXBo/Project.toml`
  [3e5feb82] SDeMo v1.7.1
  [10745b16] Statistics v1.11.1
  [f8b46487] TestItemRunner v1.1.4
  [8dfed614] Test v1.11.0
      Status `/tmp/jl_u5kXBo/Manifest.toml`
  [66dad0bd] AliasTables v1.1.3
  [7d9fca2a] Arpack v0.5.4
  [9a962f9c] DataAPI v1.16.0
  [864edb3b] DataStructures v0.19.3
  [31c24e10] Distributions v0.25.123
  [ffbed154] DocStringExtensions v0.9.5
  [1a297f60] FillArrays v1.16.0
  [34004b35] HypergeometricFunctions v0.3.28
  [92d709cd] IrrationalConstants v0.2.6
  [692b3bcd] JLLWrappers v1.7.1
  [682c06a0] JSON v1.4.0
  [2ab3a3ac] LogExpFunctions v0.3.29
  [e1d29d7a] Missings v1.2.0
  [6f286f6a] MultivariateStats v0.10.3
  [bac558e1] OrderedCollections v1.8.1
  [90014a1f] PDMats v0.11.37
  [69de0a69] Parsers v2.8.3
  [aea7be01] PrecompileTools v1.3.3
  [21216c6a] Preferences v1.5.1
  [43287f4e] PtrArrays v1.3.0
  [1fd47b50] QuadGK v2.11.2
  [189a3867] Reexport v1.2.2
  [79098fc4] Rmath v0.9.0
  [3e5feb82] SDeMo v1.7.1
  [a2af1166] SortingAlgorithms v1.2.2
  [276daf66] SpecialFunctions v2.7.1
  [10745b16] Statistics v1.11.1
  [82ae8749] StatsAPI v1.8.0
  [2913bbd2] StatsBase v0.34.10
  [4c63d2b9] StatsFuns v1.5.2
  [ec057cc2] StructUtils v2.6.2
  [f8b46487] TestItemRunner v1.1.4
  [1c621080] TestItems v1.0.0
⌅ [68821587] Arpack_jll v3.5.2+0
  [efe28fd5] OpenSpecFun_jll v0.5.6+0
  [f50d1b31] Rmath_jll v0.5.1+0
  [0dad84c5] ArgTools v1.1.2
  [56f22d72] Artifacts v1.11.0
  [2a0f44e3] Base64 v1.11.0
  [ade2ca70] Dates v1.11.0
  [f43a241f] Downloads v1.7.0
  [7b1f6079] FileWatching v1.11.0
  [b77e0a4c] InteractiveUtils v1.11.0
  [ac6e5ff7] JuliaSyntaxHighlighting v1.13.0
  [b27032c2] LibCURL v1.0.0
  [76f85450] LibGit2 v1.11.0
  [8f399da3] Libdl v1.11.0
  [37e2e46d] LinearAlgebra v1.13.0
  [56ddb016] Logging v1.11.0
  [d6f4376e] Markdown v1.11.0
  [ca575930] NetworkOptions v1.3.0
  [44cfe95a] Pkg v1.14.0
  [de0858da] Printf v1.11.0
  [9a3f8284] Random v1.11.0
  [ea8e919c] SHA v1.0.0
  [9e88b42a] Serialization v1.11.0
  [2f01184e] SparseArrays v1.13.0
  [f489334b] StyledStrings v1.13.0
  [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 v1.3.0+1
  [deac9b47] LibCURL_jll v8.18.0+0
  [e37daf67] LibGit2_jll v1.9.2+0
  [29816b5a] LibSSH2_jll v1.11.3+1
  [14a3606d] MozillaCACerts_jll v2025.12.2
  [4536629a] OpenBLAS_jll v0.3.30+0
  [05823500] OpenLibm_jll v0.8.7+0
  [458c3c95] OpenSSL_jll v3.5.5+0
  [efcefdf7] PCRE2_jll v10.47.0+0
  [bea87d4a] SuiteSparse_jll v7.10.1+0
  [83775a58] Zlib_jll v1.3.1+2
  [3161d3a3] Zstd_jll v1.5.7+1
  [8e850b90] libblastrampoline_jll v5.15.0+0
  [8e850ede] nghttp2_jll v1.68.0+1
  [3f19e933] p7zip_jll v17.7.0+0
        Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading.
     Testing Running tests...
[ Info: [  0%] LOSS: training ≈ 0.4975 validation ≈ 0.4891 ( 98%)
[ Info: [  1%] LOSS: training ≈ 0.4236 validation ≈ 0.4231 (100%)
[ Info: [  2%] LOSS: training ≈ 0.3877 validation ≈ 0.3897 (101%)
[ Info: [  2%] LOSS: training ≈ 0.3665 validation ≈ 0.3693 (101%)
[ Info: [  2%] LOSS: training ≈ 0.3524 validation ≈ 0.3553 (101%)
[ Info: [  3%] LOSS: training ≈ 0.3423 validation ≈ 0.3448 (101%)
[ Info: [  4%] LOSS: training ≈ 0.3346 validation ≈ 0.3366 (101%)
[ Info: [  4%] LOSS: training ≈ 0.3286 validation ≈ 0.3299 (100%)
[ Info: [  4%] LOSS: training ≈ 0.3236 validation ≈ 0.3243 (100%)
[ Info: [  5%] LOSS: training ≈ 0.3193 validation ≈ 0.3195 (100%)
[ Info: [  6%] LOSS: training ≈ 0.3157 validation ≈ 0.3152 (100%)
[ Info: [  6%] LOSS: training ≈ 0.3126 validation ≈ 0.3115 (100%)
[ Info: [  6%] LOSS: training ≈ 0.3098 validation ≈ 0.3082 ( 99%)
[ Info: [  7%] LOSS: training ≈ 0.3072 validation ≈ 0.3052 ( 99%)
[ Info: [  8%] LOSS: training ≈ 0.305  validation ≈ 0.3024 ( 99%)
[ Info: [  8%] LOSS: training ≈ 0.3029 validation ≈ 0.3    ( 99%)
[ Info: [  8%] LOSS: training ≈ 0.301  validation ≈ 0.2977 ( 99%)
[ Info: [  9%] LOSS: training ≈ 0.2992 validation ≈ 0.2955 ( 99%)
[ Info: [ 10%] LOSS: training ≈ 0.2976 validation ≈ 0.2936 ( 99%)
[ Info: [ 10%] LOSS: training ≈ 0.2961 validation ≈ 0.2918 ( 99%)
[ Info: [ 10%] LOSS: training ≈ 0.2946 validation ≈ 0.2901 ( 98%)
[ Info: [ 11%] LOSS: training ≈ 0.2933 validation ≈ 0.2885 ( 98%)
[ Info: [ 12%] LOSS: training ≈ 0.292  validation ≈ 0.287  ( 98%)
[ Info: [ 12%] LOSS: training ≈ 0.2908 validation ≈ 0.2856 ( 98%)
[ Info: [ 12%] LOSS: training ≈ 0.2897 validation ≈ 0.2842 ( 98%)
[ Info: [ 13%] LOSS: training ≈ 0.2886 validation ≈ 0.283  ( 98%)
[ Info: [ 14%] LOSS: training ≈ 0.2876 validation ≈ 0.2818 ( 98%)
[ Info: [ 14%] LOSS: training ≈ 0.2867 validation ≈ 0.2807 ( 98%)
[ Info: [ 14%] LOSS: training ≈ 0.2857 validation ≈ 0.2796 ( 98%)
[ Info: [ 15%] LOSS: training ≈ 0.2848 validation ≈ 0.2786 ( 98%)
[ Info: [ 16%] LOSS: training ≈ 0.284  validation ≈ 0.2776 ( 98%)
[ Info: [ 16%] LOSS: training ≈ 0.2832 validation ≈ 0.2767 ( 98%)
[ Info: [ 16%] LOSS: training ≈ 0.2824 validation ≈ 0.2758 ( 98%)
[ Info: [ 17%] LOSS: training ≈ 0.2816 validation ≈ 0.275  ( 98%)
[ Info: [ 18%] LOSS: training ≈ 0.2809 validation ≈ 0.2742 ( 98%)
[ Info: [ 18%] LOSS: training ≈ 0.2802 validation ≈ 0.2734 ( 98%)
[ Info: [ 18%] LOSS: training ≈ 0.2796 validation ≈ 0.2726 ( 98%)
[ Info: [ 19%] LOSS: training ≈ 0.2789 validation ≈ 0.2719 ( 97%)
[ Info: [ 20%] LOSS: training ≈ 0.2783 validation ≈ 0.2712 ( 97%)
[ Info: [ 20%] LOSS: training ≈ 0.2777 validation ≈ 0.2706 ( 97%)
[ Info: [ 20%] LOSS: training ≈ 0.2771 validation ≈ 0.2699 ( 97%)
[ Info: [ 21%] LOSS: training ≈ 0.2766 validation ≈ 0.2693 ( 97%)
[ Info: [ 22%] LOSS: training ≈ 0.276  validation ≈ 0.2687 ( 97%)
[ Info: [ 22%] LOSS: training ≈ 0.2755 validation ≈ 0.2682 ( 97%)
[ Info: [ 22%] LOSS: training ≈ 0.275  validation ≈ 0.2676 ( 97%)
[ Info: [ 23%] LOSS: training ≈ 0.2745 validation ≈ 0.2671 ( 97%)
[ Info: [ 24%] LOSS: training ≈ 0.2741 validation ≈ 0.2666 ( 97%)
[ Info: [ 24%] LOSS: training ≈ 0.2736 validation ≈ 0.2661 ( 97%)
[ Info: [ 24%] LOSS: training ≈ 0.2732 validation ≈ 0.2656 ( 97%)
[ Info: [ 25%] LOSS: training ≈ 0.2728 validation ≈ 0.2651 ( 97%)
[ Info: [ 26%] LOSS: training ≈ 0.2724 validation ≈ 0.2647 ( 97%)
[ Info: [ 26%] LOSS: training ≈ 0.272  validation ≈ 0.2643 ( 97%)
[ Info: [ 26%] LOSS: training ≈ 0.2716 validation ≈ 0.2638 ( 97%)
[ Info: [ 27%] LOSS: training ≈ 0.2712 validation ≈ 0.2634 ( 97%)
[ Info: [ 28%] LOSS: training ≈ 0.2708 validation ≈ 0.263  ( 97%)
[ Info: [ 28%] LOSS: training ≈ 0.2705 validation ≈ 0.2627 ( 97%)
[ Info: [ 28%] LOSS: training ≈ 0.2701 validation ≈ 0.2623 ( 97%)
[ Info: [ 29%] LOSS: training ≈ 0.2698 validation ≈ 0.2619 ( 97%)
[ Info: [ 30%] LOSS: training ≈ 0.2695 validation ≈ 0.2616 ( 97%)
[ Info: [ 30%] LOSS: training ≈ 0.2692 validation ≈ 0.2612 ( 97%)
[ Info: [ 30%] LOSS: training ≈ 0.2689 validation ≈ 0.2609 ( 97%)
[ Info: [ 31%] LOSS: training ≈ 0.2686 validation ≈ 0.2606 ( 97%)
[ Info: [ 32%] LOSS: training ≈ 0.2683 validation ≈ 0.2603 ( 97%)
[ Info: [ 32%] LOSS: training ≈ 0.268  validation ≈ 0.26   ( 97%)
[ Info: [ 32%] LOSS: training ≈ 0.2677 validation ≈ 0.2597 ( 97%)
[ Info: [ 33%] LOSS: training ≈ 0.2675 validation ≈ 0.2594 ( 97%)
[ Info: [ 34%] LOSS: training ≈ 0.2672 validation ≈ 0.2591 ( 97%)
[ Info: [ 34%] LOSS: training ≈ 0.267  validation ≈ 0.2588 ( 97%)
[ Info: [ 34%] LOSS: training ≈ 0.2667 validation ≈ 0.2586 ( 97%)
[ Info: [ 35%] LOSS: training ≈ 0.2665 validation ≈ 0.2583 ( 97%)
[ Info: [ 36%] LOSS: training ≈ 0.2663 validation ≈ 0.2581 ( 97%)
[ Info: [ 36%] LOSS: training ≈ 0.266  validation ≈ 0.2578 ( 97%)
[ Info: [ 36%] LOSS: training ≈ 0.2658 validation ≈ 0.2576 ( 97%)
[ Info: [ 37%] LOSS: training ≈ 0.2656 validation ≈ 0.2574 ( 97%)
[ Info: [ 38%] LOSS: training ≈ 0.2654 validation ≈ 0.2571 ( 97%)
[ Info: [ 38%] LOSS: training ≈ 0.2652 validation ≈ 0.2569 ( 97%)
[ Info: [ 38%] LOSS: training ≈ 0.265  validation ≈ 0.2567 ( 97%)
[ Info: [ 39%] LOSS: training ≈ 0.2648 validation ≈ 0.2565 ( 97%)
[ Info: [ 40%] LOSS: training ≈ 0.2646 validation ≈ 0.2563 ( 97%)
[ Info: [ 40%] LOSS: training ≈ 0.2644 validation ≈ 0.2561 ( 97%)
[ Info: [ 40%] LOSS: training ≈ 0.2643 validation ≈ 0.2559 ( 97%)
[ Info: [ 41%] LOSS: training ≈ 0.2641 validation ≈ 0.2557 ( 97%)
[ Info: [ 42%] LOSS: training ≈ 0.2639 validation ≈ 0.2555 ( 97%)
[ Info: [ 42%] LOSS: training ≈ 0.2638 validation ≈ 0.2553 ( 97%)
[ Info: [ 42%] LOSS: training ≈ 0.2636 validation ≈ 0.2551 ( 97%)
[ Info: [ 43%] LOSS: training ≈ 0.2635 validation ≈ 0.255  ( 97%)
[ Info: [ 44%] LOSS: training ≈ 0.2633 validation ≈ 0.2548 ( 97%)
[ Info: [ 44%] LOSS: training ≈ 0.2632 validation ≈ 0.2546 ( 97%)
[ Info: [ 44%] LOSS: training ≈ 0.263  validation ≈ 0.2545 ( 97%)
[ Info: [ 45%] LOSS: training ≈ 0.2629 validation ≈ 0.2543 ( 97%)
[ Info: [ 46%] LOSS: training ≈ 0.2627 validation ≈ 0.2541 ( 97%)
[ Info: [ 46%] LOSS: training ≈ 0.2626 validation ≈ 0.254  ( 97%)
[ Info: [ 46%] LOSS: training ≈ 0.2625 validation ≈ 0.2538 ( 97%)
[ Info: [ 47%] LOSS: training ≈ 0.2623 validation ≈ 0.2537 ( 97%)
[ Info: [ 48%] LOSS: training ≈ 0.2622 validation ≈ 0.2536 ( 97%)
[ Info: [ 48%] LOSS: training ≈ 0.2621 validation ≈ 0.2534 ( 97%)
[ Info: [ 48%] LOSS: training ≈ 0.262  validation ≈ 0.2533 ( 97%)
[ Info: [ 49%] LOSS: training ≈ 0.2619 validation ≈ 0.2531 ( 97%)
[ Info: [ 50%] LOSS: training ≈ 0.2617 validation ≈ 0.253  ( 97%)
[ Info: [ 50%] LOSS: training ≈ 0.2616 validation ≈ 0.2529 ( 97%)
[ Info: [ 50%] LOSS: training ≈ 0.2615 validation ≈ 0.2528 ( 97%)
[ Info: [ 51%] LOSS: training ≈ 0.2614 validation ≈ 0.2526 ( 97%)
[ Info: [ 52%] LOSS: training ≈ 0.2613 validation ≈ 0.2525 ( 97%)
[ Info: [ 52%] LOSS: training ≈ 0.2612 validation ≈ 0.2524 ( 97%)
[ Info: [ 52%] LOSS: training ≈ 0.2611 validation ≈ 0.2523 ( 97%)
[ Info: [ 53%] LOSS: training ≈ 0.261  validation ≈ 0.2522 ( 97%)
[ Info: [ 54%] LOSS: training ≈ 0.2609 validation ≈ 0.2521 ( 97%)
[ Info: [ 54%] LOSS: training ≈ 0.2608 validation ≈ 0.2519 ( 97%)
[ Info: [ 55%] LOSS: training ≈ 0.2607 validation ≈ 0.2518 ( 97%)
[ Info: [ 55%] LOSS: training ≈ 0.2607 validation ≈ 0.2517 ( 97%)
[ Info: [ 56%] LOSS: training ≈ 0.2606 validation ≈ 0.2516 ( 97%)
[ Info: [ 56%] LOSS: training ≈ 0.2605 validation ≈ 0.2515 ( 97%)
[ Info: [ 56%] LOSS: training ≈ 0.2604 validation ≈ 0.2514 ( 97%)
[ Info: [ 57%] LOSS: training ≈ 0.2603 validation ≈ 0.2513 ( 97%)
[ Info: [ 57%] LOSS: training ≈ 0.2602 validation ≈ 0.2512 ( 97%)
[ Info: [ 58%] LOSS: training ≈ 0.2602 validation ≈ 0.2511 ( 97%)
[ Info: [ 58%] LOSS: training ≈ 0.2601 validation ≈ 0.251  ( 97%)
[ Info: [ 59%] LOSS: training ≈ 0.26   validation ≈ 0.2509 ( 97%)
[ Info: [ 60%] LOSS: training ≈ 0.2599 validation ≈ 0.2509 ( 97%)
[ Info: [ 60%] LOSS: training ≈ 0.2599 validation ≈ 0.2508 ( 97%)
[ Info: [ 60%] LOSS: training ≈ 0.2598 validation ≈ 0.2507 ( 96%)
[ Info: [ 61%] LOSS: training ≈ 0.2597 validation ≈ 0.2506 ( 96%)
[ Info: [ 62%] LOSS: training ≈ 0.2597 validation ≈ 0.2505 ( 96%)
[ Info: [ 62%] LOSS: training ≈ 0.2596 validation ≈ 0.2504 ( 96%)
[ Info: [ 62%] LOSS: training ≈ 0.2595 validation ≈ 0.2504 ( 96%)
[ Info: [ 63%] LOSS: training ≈ 0.2595 validation ≈ 0.2503 ( 96%)
[ Info: [ 64%] LOSS: training ≈ 0.2594 validation ≈ 0.2502 ( 96%)
[ Info: [ 64%] LOSS: training ≈ 0.2593 validation ≈ 0.2501 ( 96%)
[ Info: [ 64%] LOSS: training ≈ 0.2593 validation ≈ 0.25   ( 96%)
[ Info: [ 65%] LOSS: training ≈ 0.2592 validation ≈ 0.25   ( 96%)
[ Info: [ 66%] LOSS: training ≈ 0.2592 validation ≈ 0.2499 ( 96%)
[ Info: [ 66%] LOSS: training ≈ 0.2591 validation ≈ 0.2498 ( 96%)
[ Info: [ 66%] LOSS: training ≈ 0.2591 validation ≈ 0.2498 ( 96%)
[ Info: [ 67%] LOSS: training ≈ 0.259  validation ≈ 0.2497 ( 96%)
[ Info: [ 68%] LOSS: training ≈ 0.2589 validation ≈ 0.2496 ( 96%)
[ Info: [ 68%] LOSS: training ≈ 0.2589 validation ≈ 0.2495 ( 96%)
[ Info: [ 68%] LOSS: training ≈ 0.2588 validation ≈ 0.2495 ( 96%)
[ Info: [ 69%] LOSS: training ≈ 0.2588 validation ≈ 0.2494 ( 96%)
[ Info: [ 70%] LOSS: training ≈ 0.2587 validation ≈ 0.2494 ( 96%)
[ Info: [ 70%] LOSS: training ≈ 0.2587 validation ≈ 0.2493 ( 96%)
[ Info: [ 70%] LOSS: training ≈ 0.2586 validation ≈ 0.2492 ( 96%)
[ Info: [ 71%] LOSS: training ≈ 0.2586 validation ≈ 0.2492 ( 96%)
[ Info: [ 72%] LOSS: training ≈ 0.2586 validation ≈ 0.2491 ( 96%)
[ Info: [ 72%] LOSS: training ≈ 0.2585 validation ≈ 0.249  ( 96%)
[ Info: [ 72%] LOSS: training ≈ 0.2585 validation ≈ 0.249  ( 96%)
[ Info: [ 73%] LOSS: training ≈ 0.2584 validation ≈ 0.2489 ( 96%)
[ Info: [ 74%] LOSS: training ≈ 0.2584 validation ≈ 0.2489 ( 96%)
[ Info: [ 74%] LOSS: training ≈ 0.2583 validation ≈ 0.2488 ( 96%)
[ Info: [ 74%] LOSS: training ≈ 0.2583 validation ≈ 0.2488 ( 96%)
[ Info: [ 75%] LOSS: training ≈ 0.2583 validation ≈ 0.2487 ( 96%)
[ Info: [ 76%] LOSS: training ≈ 0.2582 validation ≈ 0.2487 ( 96%)
[ Info: [ 76%] LOSS: training ≈ 0.2582 validation ≈ 0.2486 ( 96%)
[ Info: [ 76%] LOSS: training ≈ 0.2581 validation ≈ 0.2485 ( 96%)
[ Info: [ 77%] LOSS: training ≈ 0.2581 validation ≈ 0.2485 ( 96%)
[ Info: [ 78%] LOSS: training ≈ 0.2581 validation ≈ 0.2484 ( 96%)
[ Info: [ 78%] LOSS: training ≈ 0.258  validation ≈ 0.2484 ( 96%)
[ Info: [ 78%] LOSS: training ≈ 0.258  validation ≈ 0.2483 ( 96%)
[ Info: [ 79%] LOSS: training ≈ 0.258  validation ≈ 0.2483 ( 96%)
[ Info: [ 80%] LOSS: training ≈ 0.2579 validation ≈ 0.2483 ( 96%)
[ Info: [ 80%] LOSS: training ≈ 0.2579 validation ≈ 0.2482 ( 96%)
[ Info: [ 80%] LOSS: training ≈ 0.2579 validation ≈ 0.2482 ( 96%)
[ Info: [ 81%] LOSS: training ≈ 0.2578 validation ≈ 0.2481 ( 96%)
[ Info: [ 82%] LOSS: training ≈ 0.2578 validation ≈ 0.2481 ( 96%)
[ Info: [ 82%] LOSS: training ≈ 0.2578 validation ≈ 0.248  ( 96%)
[ Info: [ 82%] LOSS: training ≈ 0.2577 validation ≈ 0.248  ( 96%)
[ Info: [ 83%] LOSS: training ≈ 0.2577 validation ≈ 0.2479 ( 96%)
[ Info: [ 84%] LOSS: training ≈ 0.2577 validation ≈ 0.2479 ( 96%)
[ Info: [ 84%] LOSS: training ≈ 0.2577 validation ≈ 0.2478 ( 96%)
[ Info: [ 84%] LOSS: training ≈ 0.2576 validation ≈ 0.2478 ( 96%)
[ Info: [ 85%] LOSS: training ≈ 0.2576 validation ≈ 0.2478 ( 96%)
[ Info: [ 86%] LOSS: training ≈ 0.2576 validation ≈ 0.2477 ( 96%)
[ Info: [ 86%] LOSS: training ≈ 0.2575 validation ≈ 0.2477 ( 96%)
[ Info: [ 86%] LOSS: training ≈ 0.2575 validation ≈ 0.2476 ( 96%)
[ Info: [ 87%] LOSS: training ≈ 0.2575 validation ≈ 0.2476 ( 96%)
[ Info: [ 88%] LOSS: training ≈ 0.2575 validation ≈ 0.2476 ( 96%)
[ Info: [ 88%] LOSS: training ≈ 0.2574 validation ≈ 0.2475 ( 96%)
[ Info: [ 88%] LOSS: training ≈ 0.2574 validation ≈ 0.2475 ( 96%)
[ Info: [ 89%] LOSS: training ≈ 0.2574 validation ≈ 0.2475 ( 96%)
[ Info: [ 90%] LOSS: training ≈ 0.2574 validation ≈ 0.2474 ( 96%)
[ Info: [ 90%] LOSS: training ≈ 0.2573 validation ≈ 0.2474 ( 96%)
[ Info: [ 90%] LOSS: training ≈ 0.2573 validation ≈ 0.2473 ( 96%)
[ Info: [ 91%] LOSS: training ≈ 0.2573 validation ≈ 0.2473 ( 96%)
[ Info: [ 92%] LOSS: training ≈ 0.2573 validation ≈ 0.2473 ( 96%)
[ Info: [ 92%] LOSS: training ≈ 0.2572 validation ≈ 0.2472 ( 96%)
[ Info: [ 92%] LOSS: training ≈ 0.2572 validation ≈ 0.2472 ( 96%)
[ Info: [ 93%] LOSS: training ≈ 0.2572 validation ≈ 0.2472 ( 96%)
[ Info: [ 94%] LOSS: training ≈ 0.2572 validation ≈ 0.2471 ( 96%)
[ Info: [ 94%] LOSS: training ≈ 0.2572 validation ≈ 0.2471 ( 96%)
[ Info: [ 94%] LOSS: training ≈ 0.2571 validation ≈ 0.2471 ( 96%)
[ Info: [ 95%] LOSS: training ≈ 0.2571 validation ≈ 0.247  ( 96%)
[ Info: [ 96%] LOSS: training ≈ 0.2571 validation ≈ 0.247  ( 96%)
[ Info: [ 96%] LOSS: training ≈ 0.2571 validation ≈ 0.247  ( 96%)
[ Info: [ 96%] LOSS: training ≈ 0.2571 validation ≈ 0.2469 ( 96%)
[ Info: [ 97%] LOSS: training ≈ 0.257  validation ≈ 0.2469 ( 96%)
[ Info: [ 98%] LOSS: training ≈ 0.257  validation ≈ 0.2469 ( 96%)
[ Info: [ 98%] LOSS: training ≈ 0.257  validation ≈ 0.2469 ( 96%)
[ Info: [ 98%] LOSS: training ≈ 0.257  validation ≈ 0.2468 ( 96%)
[ Info: [ 99%] LOSS: training ≈ 0.257  validation ≈ 0.2468 ( 96%)
[ Info: [100%] LOSS: training ≈ 0.2569 validation ≈ 0.2468 ( 96%)
[ Info: [100%] LOSS: training ≈ 0.2569 validation ≈ 0.2467 ( 96%)
[ Info: [  0%] LOSS: training ≈ 0.4747
[ Info: [  1%] LOSS: training ≈ 0.4038
[ Info: [  2%] LOSS: training ≈ 0.371 
[ Info: [  2%] LOSS: training ≈ 0.352 
[ Info: [  2%] LOSS: training ≈ 0.3394
[ Info: [  3%] LOSS: training ≈ 0.3303
[ Info: [  4%] LOSS: training ≈ 0.3233
[ Info: [  4%] LOSS: training ≈ 0.3178
[ Info: [  4%] LOSS: training ≈ 0.3132
[ Info: [  5%] LOSS: training ≈ 0.3093
[ Info: [  6%] LOSS: training ≈ 0.3059
[ Info: [  6%] LOSS: training ≈ 0.303 
[ Info: [  6%] LOSS: training ≈ 0.3004
[ Info: [  7%] LOSS: training ≈ 0.298 
[ Info: [  8%] LOSS: training ≈ 0.2959
[ Info: [  8%] LOSS: training ≈ 0.2939
[ Info: [  8%] LOSS: training ≈ 0.2921
[ Info: [  9%] LOSS: training ≈ 0.2905
[ Info: [ 10%] LOSS: training ≈ 0.289 
[ Info: [ 10%] LOSS: training ≈ 0.2875
[ Info: [ 10%] LOSS: training ≈ 0.2862
[ Info: [ 11%] LOSS: training ≈ 0.2849
[ Info: [ 12%] LOSS: training ≈ 0.2838
[ Info: [ 12%] LOSS: training ≈ 0.2826
[ Info: [ 12%] LOSS: training ≈ 0.2816
[ Info: [ 13%] LOSS: training ≈ 0.2806
[ Info: [ 14%] LOSS: training ≈ 0.2796
[ Info: [ 14%] LOSS: training ≈ 0.2787
[ Info: [ 14%] LOSS: training ≈ 0.2779
[ Info: [ 15%] LOSS: training ≈ 0.277 
[ Info: [ 16%] LOSS: training ≈ 0.2763
[ Info: [ 16%] LOSS: training ≈ 0.2755
[ Info: [ 16%] LOSS: training ≈ 0.2748
[ Info: [ 17%] LOSS: training ≈ 0.2741
[ Info: [ 18%] LOSS: training ≈ 0.2734
[ Info: [ 18%] LOSS: training ≈ 0.2728
[ Info: [ 18%] LOSS: training ≈ 0.2722
[ Info: [ 19%] LOSS: training ≈ 0.2716
[ Info: [ 20%] LOSS: training ≈ 0.271 
[ Info: [ 20%] LOSS: training ≈ 0.2705
[ Info: [ 20%] LOSS: training ≈ 0.27  
[ Info: [ 21%] LOSS: training ≈ 0.2695
[ Info: [ 22%] LOSS: training ≈ 0.269 
[ Info: [ 22%] LOSS: training ≈ 0.2685
[ Info: [ 22%] LOSS: training ≈ 0.2681
[ Info: [ 23%] LOSS: training ≈ 0.2677
[ Info: [ 24%] LOSS: training ≈ 0.2673
[ Info: [ 24%] LOSS: training ≈ 0.2669
[ Info: [ 24%] LOSS: training ≈ 0.2665
[ Info: [ 25%] LOSS: training ≈ 0.2661
[ Info: [ 26%] LOSS: training ≈ 0.2657
[ Info: [ 26%] LOSS: training ≈ 0.2654
[ Info: [ 26%] LOSS: training ≈ 0.265 
[ Info: [ 27%] LOSS: training ≈ 0.2647
[ Info: [ 28%] LOSS: training ≈ 0.2644
[ Info: [ 28%] LOSS: training ≈ 0.2641
[ Info: [ 28%] LOSS: training ≈ 0.2638
[ Info: [ 29%] LOSS: training ≈ 0.2635
[ Info: [ 30%] LOSS: training ≈ 0.2632
[ Info: [ 30%] LOSS: training ≈ 0.263 
[ Info: [ 30%] LOSS: training ≈ 0.2627
[ Info: [ 31%] LOSS: training ≈ 0.2625
[ Info: [ 32%] LOSS: training ≈ 0.2622
[ Info: [ 32%] LOSS: training ≈ 0.262 
[ Info: [ 32%] LOSS: training ≈ 0.2618
[ Info: [ 33%] LOSS: training ≈ 0.2615
[ Info: [ 34%] LOSS: training ≈ 0.2613
[ Info: [ 34%] LOSS: training ≈ 0.2611
[ Info: [ 34%] LOSS: training ≈ 0.2609
[ Info: [ 35%] LOSS: training ≈ 0.2607
[ Info: [ 36%] LOSS: training ≈ 0.2605
[ Info: [ 36%] LOSS: training ≈ 0.2603
[ Info: [ 36%] LOSS: training ≈ 0.2602
[ Info: [ 37%] LOSS: training ≈ 0.26  
[ Info: [ 38%] LOSS: training ≈ 0.2598
[ Info: [ 38%] LOSS: training ≈ 0.2597
[ Info: [ 38%] LOSS: training ≈ 0.2595
[ Info: [ 39%] LOSS: training ≈ 0.2593
[ Info: [ 40%] LOSS: training ≈ 0.2592
[ Info: [ 40%] LOSS: training ≈ 0.259 
[ Info: [ 40%] LOSS: training ≈ 0.2589
[ Info: [ 41%] LOSS: training ≈ 0.2588
[ Info: [ 42%] LOSS: training ≈ 0.2586
[ Info: [ 42%] LOSS: training ≈ 0.2585
[ Info: [ 42%] LOSS: training ≈ 0.2584
[ Info: [ 43%] LOSS: training ≈ 0.2582
[ Info: [ 44%] LOSS: training ≈ 0.2581
[ Info: [ 44%] LOSS: training ≈ 0.258 
[ Info: [ 44%] LOSS: training ≈ 0.2579
[ Info: [ 45%] LOSS: training ≈ 0.2578
[ Info: [ 46%] LOSS: training ≈ 0.2577
[ Info: [ 46%] LOSS: training ≈ 0.2576
[ Info: [ 46%] LOSS: training ≈ 0.2574
[ Info: [ 47%] LOSS: training ≈ 0.2573
[ Info: [ 48%] LOSS: training ≈ 0.2572
[ Info: [ 48%] LOSS: training ≈ 0.2572
[ Info: [ 48%] LOSS: training ≈ 0.2571
[ Info: [ 49%] LOSS: training ≈ 0.257 
[ Info: [ 50%] LOSS: training ≈ 0.2569
[ Info: [ 50%] LOSS: training ≈ 0.2568
[ Info: [ 50%] LOSS: training ≈ 0.2567
[ Info: [ 51%] LOSS: training ≈ 0.2566
[ Info: [ 52%] LOSS: training ≈ 0.2565
[ Info: [ 52%] LOSS: training ≈ 0.2565
[ Info: [ 52%] LOSS: training ≈ 0.2564
[ Info: [ 53%] LOSS: training ≈ 0.2563
[ Info: [ 54%] LOSS: training ≈ 0.2562
[ Info: [ 54%] LOSS: training ≈ 0.2562
[ Info: [ 55%] LOSS: training ≈ 0.2561
[ Info: [ 55%] LOSS: training ≈ 0.256 
[ Info: [ 56%] LOSS: training ≈ 0.256 
[ Info: [ 56%] LOSS: training ≈ 0.2559
[ Info: [ 56%] LOSS: training ≈ 0.2558
[ Info: [ 57%] LOSS: training ≈ 0.2558
[ Info: [ 57%] LOSS: training ≈ 0.2557
[ Info: [ 58%] LOSS: training ≈ 0.2556
[ Info: [ 58%] LOSS: training ≈ 0.2556
[ Info: [ 59%] LOSS: training ≈ 0.2555
[ Info: [ 60%] LOSS: training ≈ 0.2555
[ Info: [ 60%] LOSS: training ≈ 0.2554
[ Info: [ 60%] LOSS: training ≈ 0.2554
[ Info: [ 61%] LOSS: training ≈ 0.2553
[ Info: [ 62%] LOSS: training ≈ 0.2553
[ Info: [ 62%] LOSS: training ≈ 0.2552
[ Info: [ 62%] LOSS: training ≈ 0.2552
[ Info: [ 63%] LOSS: training ≈ 0.2551
[ Info: [ 64%] LOSS: training ≈ 0.2551
[ Info: [ 64%] LOSS: training ≈ 0.255 
[ Info: [ 64%] LOSS: training ≈ 0.255 
[ Info: [ 65%] LOSS: training ≈ 0.2549
[ Info: [ 66%] LOSS: training ≈ 0.2549
[ Info: [ 66%] LOSS: training ≈ 0.2548
[ Info: [ 66%] LOSS: training ≈ 0.2548
[ Info: [ 67%] LOSS: training ≈ 0.2548
[ Info: [ 68%] LOSS: training ≈ 0.2547
[ Info: [ 68%] LOSS: training ≈ 0.2547
[ Info: [ 68%] LOSS: training ≈ 0.2546
[ Info: [ 69%] LOSS: training ≈ 0.2546
[ Info: [ 70%] LOSS: training ≈ 0.2546
[ Info: [ 70%] LOSS: training ≈ 0.2545
[ Info: [ 70%] LOSS: training ≈ 0.2545
[ Info: [ 71%] LOSS: training ≈ 0.2545
[ Info: [ 72%] LOSS: training ≈ 0.2544
[ Info: [ 72%] LOSS: training ≈ 0.2544
[ Info: [ 72%] LOSS: training ≈ 0.2544
[ Info: [ 73%] LOSS: training ≈ 0.2543
[ Info: [ 74%] LOSS: training ≈ 0.2543
[ Info: [ 74%] LOSS: training ≈ 0.2543
[ Info: [ 74%] LOSS: training ≈ 0.2542
[ Info: [ 75%] LOSS: training ≈ 0.2542
[ Info: [ 76%] LOSS: training ≈ 0.2542
[ Info: [ 76%] LOSS: training ≈ 0.2541
[ Info: [ 76%] LOSS: training ≈ 0.2541
[ Info: [ 77%] LOSS: training ≈ 0.2541
[ Info: [ 78%] LOSS: training ≈ 0.2541
[ Info: [ 78%] LOSS: training ≈ 0.254 
[ Info: [ 78%] LOSS: training ≈ 0.254 
[ Info: [ 79%] LOSS: training ≈ 0.254 
[ Info: [ 80%] LOSS: training ≈ 0.254 
[ Info: [ 80%] LOSS: training ≈ 0.2539
[ Info: [ 80%] LOSS: training ≈ 0.2539
[ Info: [ 81%] LOSS: training ≈ 0.2539
[ Info: [ 82%] LOSS: training ≈ 0.2539
[ Info: [ 82%] LOSS: training ≈ 0.2538
[ Info: [ 82%] LOSS: training ≈ 0.2538
[ Info: [ 83%] LOSS: training ≈ 0.2538
[ Info: [ 84%] LOSS: training ≈ 0.2538
[ Info: [ 84%] LOSS: training ≈ 0.2537
[ Info: [ 84%] LOSS: training ≈ 0.2537
[ Info: [ 85%] LOSS: training ≈ 0.2537
[ Info: [ 86%] LOSS: training ≈ 0.2537
[ Info: [ 86%] LOSS: training ≈ 0.2537
[ Info: [ 86%] LOSS: training ≈ 0.2536
[ Info: [ 87%] LOSS: training ≈ 0.2536
[ Info: [ 88%] LOSS: training ≈ 0.2536
[ Info: [ 88%] LOSS: training ≈ 0.2536
[ Info: [ 88%] LOSS: training ≈ 0.2536
[ Info: [ 89%] LOSS: training ≈ 0.2535
[ Info: [ 90%] LOSS: training ≈ 0.2535
[ Info: [ 90%] LOSS: training ≈ 0.2535
[ Info: [ 90%] LOSS: training ≈ 0.2535
[ Info: [ 91%] LOSS: training ≈ 0.2535
[ Info: [ 92%] LOSS: training ≈ 0.2535
[ Info: [ 92%] LOSS: training ≈ 0.2534
[ Info: [ 92%] LOSS: training ≈ 0.2534
[ Info: [ 93%] LOSS: training ≈ 0.2534
[ Info: [ 94%] LOSS: training ≈ 0.2534
[ Info: [ 94%] LOSS: training ≈ 0.2534
[ Info: [ 94%] LOSS: training ≈ 0.2534
[ Info: [ 95%] LOSS: training ≈ 0.2533
[ Info: [ 96%] LOSS: training ≈ 0.2533
[ Info: [ 96%] LOSS: training ≈ 0.2533
[ Info: [ 96%] LOSS: training ≈ 0.2533
[ Info: [ 97%] LOSS: training ≈ 0.2533
[ Info: [ 98%] LOSS: training ≈ 0.2533
[ Info: [ 98%] LOSS: training ≈ 0.2533
[ Info: [ 98%] LOSS: training ≈ 0.2532
[ Info: [ 99%] LOSS: training ≈ 0.2532
[ Info: [100%] LOSS: training ≈ 0.2532
[ Info: [100%] LOSS: training ≈ 0.2532
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
[ Info: Baseline mcc: -2.8367742685852565e-17
[ Info: Optimal 1 variables model - mcc ≈ 0.7557
[ Info: Optimal 2 variables model - mcc ≈ 0.7649
[ Info: Optimal 3 variables model - mcc ≈ 0.782
[ Info: Optimal 4 variables model - mcc ≈ 0.7866
[ Info: Optimal 5 variables model - mcc ≈ 0.8018
[ Info: Returning model with 5 variables - mcc ≈ 0.8018
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 2 vars.] MCC val. ≈ -0.0
[ Info: [ 3 vars.] MCC val. ≈ 0.761
[ Info: [ 4 vars.] MCC val. ≈ 0.78
[ Info: [ 5 vars.] MCC val. ≈ 0.794
[ Info: Optimal var. pool: [1, 12, 2, 8, 16]
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 0 vars.] MCC val. ≈ -0.0
[ Info: [ 1 vars.] MCC val. ≈ 0.765
[ Info: [ 2 vars.] MCC val. ≈ 0.78
[ Info: [ 3 vars.] MCC val. ≈ 0.808
[ Info: [ 4 vars.] MCC val. ≈ 0.822
[ Info: Optimal var. pool: [8, 3, 18, 19]
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
[ Info: [19 vars.] MCC val. ≈ -0.0
[ Info: [18 vars.] MCC val. ≈ 0.739
[ Info: [17 vars.] MCC val. ≈ 0.741
[ Info: [16 vars.] MCC val. ≈ 0.767
[ Info: [15 vars.] MCC val. ≈ 0.775
[ Info: [14 vars.] MCC val. ≈ 0.781
[ Info: Optimal var. pool: [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 19]
┌ Warning: backwardselection! will be deprecated - use variables! with BackwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:44
[ Info: [ 9 vars.] MCC val. ≈ -0.0
[ Info: [ 8 vars.] MCC val. ≈ 0.814
[ Info: [ 7 vars.] MCC val. ≈ 0.828
[ Info: [ 6 vars.] MCC val. ≈ 0.842
[ Info: Optimal var. pool: [1, 3, 5, 6, 8, 9]
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 2 vars.] MCC val. ≈ -0.0
[ Info: [ 3 vars.] MCC val. ≈ 0.749
[ Info: [ 4 vars.] MCC val. ≈ 0.762
[ Info: [ 5 vars.] MCC val. ≈ 0.77
[ Info: Optimal var. pool: [12, 13, 5, 2, 1]
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: stepwisevif! will be deprecated - use variables! with VarianceInflationFactor instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/vif.jl:24
┌ Warning: forwardselection! will be deprecated - use variables! with ForwardSelection instead
└ @ SDeMo ~/.julia/packages/SDeMo/xCpER/src/variables/selection.jl:90
[ Info: [ 0 vars.] MCC val. ≈ -0.0
[ Info: [ 1 vars.] MCC val. ≈ 0.708
[ Info: [ 2 vars.] MCC val. ≈ 0.748
[ Info: Optimal var. pool: [8, 5]
Test Summary: | Pass  Total      Time
Package       |  373    373  22m56.9s
     Testing SDeMo tests passed 

Testing completed after 1431.92s

PkgEval succeeded after 1492.51s