Handling nominal category features in decision tree
I would not say that using One-Hot-Encoder to handle nominal features is an optimal way. Even it might affect adversely your Decision Tree model in terms of performance.
Theoretically, a Decision Tree does not need a one-hot-encoding for categorical features, besides it will increase the number of computations and will result in a relatively inefficient model. Unlike non-tree algorithms (e.g. regression algorithms), a Decision Tree cannot be biased by the label encoding of categorical features because it uses splits to reach the optimal result.
Experimental results show that one-hot-encoding gives a rise to sparsity in the trees which might drastically drop the performance of the model. Here is a wonderful analysis and explanation of the effect of one-hot-encoding on Tree-based algorithms. Also, check this useful source.