Missing Categorical feature supports for HistgradientBoost

HistgradientBoost methods from Scikit-learn support native categorical features, meaning no preprocessing is needed, as shown here:
https://scikit-learn.org/stable/modules/ensemble.html#categorical-support-gbdt

However, the MLJ interface seems to enforce the input to be continuous tables, as seen in the source code:

meta(HistGradientBoostingClassifier,
    input   = Table(Continuous),
    target  = AbstractVector{<:Finite},
    weights = false
    )

Since MLJ enforces the scitype schema, the categorical feature columns should be auto-inferred. I hope this can be addressed, thanks.

On second thoughts, since scikit-learn will auto-infer categorical features based on dtype, maybe relaxing the Table type to some union type would suffice.

Start with the HistGradientBoostingClassifier meta declaration shown in the issue and compare it with Scikit-learn's categorical-support documentation. Determine which MLJ table scitypes allow categorical columns while preserving the existing target and weights constraints, then verify that categorical features can be passed without preprocessing.