Autotrain: explain generated models
Hello everyone !I am a product manager working on matching algorithms for a marketplace in the health sector, and one of the questions I often come asking myself is: do we collect the right data points to automate some part of our matching processes ?I recently started using the Autotrain feature to answer this question, with the idea that, if I can train a classifier with good performance on a given dataset, it means that we do collect the right data points. If not, it means we need to evolve our product.This has shown very good results, but I would now like to go one step further andunderstand the generated models. Typical questions I have are:assuming the best autotrained model is a decision tree, how can I understand its decision rules ?assuming it’s an xgboost classifier, can I know the feature importance that the model learned ?Any guidance on how I can answer these questions would be much appreciatedThank you in advance !