Decision tree classifiers are one of the easiest to understand ML algorithms. They are based on the scientific concept of entrophy. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

Because trees can be visualised they are simple to understand and interpret. They use a white box model. If a given situation is observable in a model, the explanation for the condition is easily explained by boolean logic. By contrast, a black box model (e.g. in an artificial neural network), results may be more difficult to interpret. This link provides a good reference to DTs in scikit learn.

To visualise DTs you can use Graphviz and pydotplus. Decision tree methods are highly versatile and useful for data exploration. Real world applications can be found in various fields including computer vision (features related to pixels), cancer applications, classifying sleep signals, drug analysis etc.