Decision trees are one of the most popular machine learning methods. One of the reasons is their easy usage and understanding. A decision tree is a method that can be easily visualized and understood. We have tens of different decision trees [1–17]. A decision tree is a method that divide the feature space on each level of a tree. It means it is a non-linear method because it does a linear classification at each node. The tree starts with a root and consists of decision nodes and leafs. It decouples the training set into smaller sets based on some conditions related to one (univariate) or more features (multivariate). As a result of the division, we can get one or more smaller data sets of different sizes. The goal of a decision tree is to build a tree in a way that we have objects of the same label in each leaf. A tree can be also written as a set of rules as it is based on a set of choices at each node. That is why it is commonly used in many decision-making software. It handles multiclass problems easily. We can use decision trees to understand which feature has the major impact on the classification. The more often a feature is used in decision nodes the higher impact it has on the classification. Compared to some other methods, decision trees do not work like a black box as hidden layers of a neural network. Another advantage of decision trees is their performance. Compared to most methods it is fast. On the other hand, a small change in training data can significantly change the rules and accuracy.