The following are the strengths of decision tree classifiers:

•  The rules of the models created by using a decision tree algorithm are interpretable by humans. Models such as this are called whitebox models. Whitebox models are a requirement whenever transparency is needed to trace the details and reasons for decisions that are made by the model. This transparency is essential in applications where we want to prevent bias and protect vulnerable communities. For example, a whitebox model is generally a requirement for critical use cases in government and insurance industries. 
• Decision tree classifiers are designed to extract information from discrete problem space. This means that most of the features are category variables, so using a decision tree to train the model is a good choice.