  Developing Machine Learning (ML) algorithms for heterogeneous/mixed data is a
longstanding problem. Many ML algorithms are not applicable to mixed data,
which include numeric and non-numeric data, text, graphs and so on to generate
interpretable models. Another longstanding problem is developing algorithms for
lossless visualization of multidimensional mixed data. The further progress in
ML heavily depends on success interpretable ML algorithms for mixed data and
lossless interpretable visualization of multidimensional data. The later allows
developing interpretable ML models using visual knowledge discovery by
end-users, who can bring valuable domain knowledge which is absent in the
training data. The challenges for mixed data include: (1) generating numeric
coding schemes for non-numeric attributes for numeric ML algorithms to provide
accurate and interpretable ML models, (2) generating methods for lossless
visualization of n-D non-numeric data and visual rule discovery in these
visualizations. This paper presents a classification of mixed data types,
analyzes their importance for ML and present the developed experimental toolkit
to deal with mixed data. It combines the Data Types Editor, VisCanvas data
visualization and rule discovery system which is available on GitHub.
