Decision tree in machine learning: Learn how to use

Learn how to use decision trees for classification and regression with scikit-learn, a Python machine learning library. See examples, advantages, disadvantages and multi-output problems of decision trees. A decision tree is a supervised learning algorithm used for both classification and regression tasks. It has a hierarchical tree structure which consists of a root node, branches, internal nodes and leaf nodes. It It works like a flowchart help to make decisions step by step where: Internal nodes represent attribute tests Branches represent attribute values Leaf nodes represent final decisions or predictions. Decision trees are widely used due to their interpretability, flexibility and low ... A Decision Tree helps us to make decisions by mapping out different choices and their possible outcomes. It’s used in machine learning for tasks like classification and prediction. In this article, we’ll see more about Decision Trees , their types and other core concepts.