As of now, MLTK does not support importing a trained model from outside of Splunk.
There are several ways that you could try:
Use random forest algorithm in MLTK to train a native MLTK model, you can just change the
max_memory_usage_mb stanza in the
mlspl.conf file to allow higher memory usage.
Write a custom algorithm that reads your trained model and translate the parameters and pass into sklearn models, then run on your data. It may not be trivial in your use case due to the complexity of decision tree.
Write a custom search command that sends Splunk data to your environment where you trained your model, make predictions and send back to Splunk. This requires good understanding of Splunk custom search command and extra integration work.