Feature request: Support for tables about mljlinearmodels.jl HOT 3 CLOSED

julia> using DataFrames, CategoricalArrays, ScientificTypes, MLJModelInterface, MLJBase

julia> X = hcat(DataFrame(randn(10, 5), :auto), DataFrame(CategoricalArray.(eachcol(rand(["1", "2", "3", "4"], 10, 5))), :auto); makeunique=true);

julia> schema(X)
┌───────┬───────────────┬──────────────────────────────────┐
│ names │ scitypes      │ types                            │
├───────┼───────────────┼──────────────────────────────────┤
│ x1    │ Continuous    │ Float64                          │
│ x2    │ Continuous    │ Float64                          │
│ x3    │ Continuous    │ Float64                          │
│ x4    │ Continuous    │ Float64                          │
│ x5    │ Continuous    │ Float64                          │
│ x1_1  │ Multiclass{4} │ CategoricalValue{String, UInt32} │
│ x2_1  │ Multiclass{4} │ CategoricalValue{String, UInt32} │
│ x3_1  │ Multiclass{3} │ CategoricalValue{String, UInt32} │
│ x4_1  │ Multiclass{4} │ CategoricalValue{String, UInt32} │
│ x5_1  │ Multiclass{4} │ CategoricalValue{String, UInt32} │
└───────┴───────────────┴──────────────────────────────────┘

julia> typeof(MLJModelInterface.matrix(X))
Matrix{Any} (alias for Array{Any, 2})

julia> tuned_machine = machine(config, x[:, Not(1)], x[:, 1]) |> fit!
ERROR: MethodError: no method matching fit(::GeneralizedLinearRegression{L2Loss, NoPenalty}, ::Matrix{Any}, ::Vector{Float64}; solver::Analytical)