As far as I know, and I've researched this issue deeply in the past, there are no predictive modeling techniques (beside trees, XgBoost, etc.) that are designed to handle both types of input at the same time without simply transforming the type of the features.
Note that algorithms like Random Forest and XGBoost accept an input of mixed features, but they apply some logic to handle them during split of a node.
Make sure you understand the logic "under the hood" and that you're OK with whatever is happening in the black-box.
Yet, distance/kernel based models (e.g., K-NN, NN regression, support vector machines) can be used to handle mixed type feature space by defining a “special” distance function. Such that, for every feature, applies an appropriate distance metric (e.g., for a numeric feature we’ll calculate the Euclidean distance of 2 numbers while for a categorical feature we’ll simple calculate the overlap distance of 2 string values).
So, the distance/similarity between user $u_1$ and $u_2$ in feature $f_i$, as follows:
$d(u_1,u_2 )_{f_i}=(dis-categorical(u_1,u_2 )_{f_i} $ if feature $f_i$ is categorical,
$d(u_1,u_2 )_{f_i}=dis-numeric(u_1,u_2 )_{f_i} $ if feature $f_i$ is numerical. and 1 if feature $f_i$ is not defined in $u_1$ or $u_2$.
Some known distance function for categorical features:
It depends a little bit on your purpose, but if you're after a visualization tool there's a trick with applying multidimensional scaling to the output of random forest proximity which can produce pretty pictures and will work for a mixture of categorical and continuous data. Here you would classify the species according to your predictors. But - and it's a big caveat - I don't know if anyone really knows what the output to these visualizations mean.
Another alternative might be to apply multidimensional scaling to something like the Gower similarity.
There's a hanging question - what's your ultimate purpose? What question do you want to answer? I like these techniques as exploratory tools to perhaps lead you to asking more and better questions, but I'm not sure what they explain or tell you by themselves.
Maybe I'm reading too much into your question, but if you want to explore which predictor variables have the values for the hybrids sitting between the two pure species, you might be better building a model to estimate the values for the predictor variables which lead to the species and the hybrids directly. If you want to measure how the variables are related to each other, perhaps build a correlation matrix - and there are many neat visualizations for this.
Best Answer
I was able to find a diagram from this page that clarified a lot of the original confusion.