doctest failure in ~/work/Maxnet.jl/Maxnet.jl/src/mlj_interface.jl:53-61 ```jldoctest using Maxnet, MLJBase p_a, env = Maxnet.bradypus() mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a)) fit!(mach) yhat = MLJBase.predict(mach, env) # output ``` Subexpression: using Maxnet, MLJBase p_a, env = Maxnet.bradypus() mach = machine(MaxnetBinaryClassifier(features = "lqp"), env, categorical(p_a)) fit!(mach) yhat = MLJBase.predict(mach, env) Evaluated output: ┌ Warning: The number and/or types of data arguments do not match what the specified model │ supports. Suppress this type check by specifying `scitype_check_level=0`. │ │ Run `@doc Maxnet.MaxnetBinaryClassifier` to learn more about your model's requirements. │ │ Commonly, but non exclusively, supervised models are constructed using the syntax │ `machine(model, X, y)` or `machine(model, X, y, w)` while most other models are │ constructed with `machine(model, X)`. Here `X` are features, `y` a target, and `w` │ sample or class weights. │ │ In general, data in `machine(model, data...)` is expected to satisfy │ │ scitype(data) <: MLJ.fit_data_scitype(model) │ │ In the present case: │ │ scitype(data) = Tuple{ScientificTypesBase.Table{Union{AbstractVector{ScientificTypesBase.Count}, AbstractVector{ScientificTypesBase.Multiclass{14}}}}, AbstractVector{ScientificTypesBase.Multiclass{2}}} │ │ fit_data_scitype(model) = Tuple{ScientificTypesBase.Table{<:Union{AbstractVector{<:ScientificTypesBase.Continuous}, AbstractVector{<:ScientificTypesBase.Finite}}}, AbstractVector{<:ScientificTypesBase.Binary}} └ @ MLJBase ~/.julia/packages/MLJBase/7nGJF/src/machines.jl:237 [ Info: Training machine(MaxnetBinaryClassifier(features = lqp, …), …). 1116-element UnivariateFiniteVector{Multiclass{2}, Bool, UInt32, Float64}: UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.812, true=>0.188) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.811, true=>0.189) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.501, true=>0.499) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.811, true=>0.189) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.752, true=>0.248) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.71, true=>0.29) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.926, true=>0.0742) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.696, true=>0.304) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.702, true=>0.298) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.649, true=>0.351) ⋮ UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.944, true=>0.0559) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.816, true=>0.184) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.998, true=>0.00152) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.997, true=>0.00325) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>1.0, true=>0.000213) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.664, true=>0.336) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.413, true=>0.587) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.922, true=>0.0777) UnivariateFinite{ScientificTypesBase.Multiclass{2}}(false=>0.999, true=>0.00146) Expected output: diff = Warning: Diff output requires color. ┌ Warning: The number and/or types of data arguments do not match what the specified model │ supports. Suppress this type check by specifying `scitype_check_level=0`. │ │ Run `@doc Maxnet.MaxnetBinaryClassifier` to learn more about your model's requirements. │ │ Commonly, but non exclusively, supervised models are constructed using the syntax │ `machine(model, X, y)` or `machine(model, X, y, w)` while most other models are │ constructed with `machine(model, X)`. Here `X` are features, `y` a target, and `w` │ sample or class weights. │ │ In general, data in `machine(model, data...)` is expected to satisfy │ │ scityp