[autokad]

in kaggle, I often turn categorical into numeric and call it a day (even if its not ordinal). I have even found that forcing ordinality (like software versions in the microsoft malware competition) usually makes things worse in hold out.

spending too much time on categoricals is a waste of time, there are other things you can improve in your limited time, and even 'doing the right thing' results in poorer performance in hold out.

catboost is great, it ensembles wonderfully with xgboost. if you find it being fragile, you probably have outliers that need droped - tree algs are really just fancy nearest neighbors so an outlier can ruin predictions considerably.

In general, lgbm trains fast and lets you try many things quickly, but almost always under performs catboost and xgboost. catboost performs really well out of the box and you can generally get results quicker than xgboost, but a well tuned xgboost is usually the best. since xgboost and catboost build trees differently and both perform really well, they make great friends in ensembles.

I have done pretty well on kaggle though I haven't invested much time, top 100 in zillow home price prediction