Decision Tree Algorithm Decision Tree algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, the decision tree algorithm can be used for solving regression and classification problems too. 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. Trees are a common analogy in everyday life. Shaped by a combination of roots, trunk, branches, and leaves, trees often symbolize growth. In machine learning, a decision tree is an algorithm that can create classification and regression models. The decision tree is so named because it starts at the root, like an upside-down tree, and branches off to demonstrate various outcomes. Because machine learning is based on solving problems, decision trees help us visualize these models and adjust ... 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.
