Using ML model and decision tree to create a new risk classification The idea of this project was to use a Machine Learning model to find the best variables to include in a decision tree algorithm. After evaluating with caret a number of different models I found the one with the best accuracy (0.88) with my database. Then, I selected the most important variables used (Importance > 50%) and I included them in a decision tree analysis done with Rpart. However, the final accuracy of my decision tree is way lower than I expected (0.62). Why is that? How can I improve my decision tree accuracy? Am I following the wrong idea in combining ML and decision tree?

Decision trees (DT) are usually a weaker classifier model. So a drop in accuracy using a DT against the unspecified best classifier is expected.
Second, limiting the features available before building the DT is unwise. If you need a DT as the final classifier let it search among all features, and if the resulting DT is too complex (whatever that means to you, in your case it seems that the number of different features used in the tree seems to be a problem), then explore using the control parameters of rpart to limit the depth of the tree.
