Классификации дерева в машинном обучении и гиперпараметрах

Machine learning algorithms play a crucial role in extracting valuable insights from data, enabling businesses and researchers to make informed decisions. One such algorithm is the decision tree, which is widely used for classification tasks. Decision tree classification utilizes a tree-like model of decisions and their potential consequences, making it an intuitive and powerful tool for solving complex problems. In this article, a model that determines which drug is suitable for a patient with a certain disease is created using the Decision tree algorithm. This problem is multi-class classification (multiclass classification) help score consolidation. Alternatively, how function, domain, and hyperparameters simplify decision tree models are explored.