Decision trees (DT) are supervised learning methods used for classification and regression. You can think of DTs as collection of if-then-else rules that create a tree structure.
DTs are white box models. You're able to visualize and understand all the decision rules.
DTs tend to overfit on data with a large number of features. Try to reduce the dimensionality before using decision trees and start with a low max depth while training.
DTs tend to get biased if training dataset classes are not balanced. Try to keep the class proportions equal in the training dataset.