Decision tree algorithm: Decision trees’ key features include

Decision trees’ key features include: A hierarchical tree-structured (Cf. Boosting: sequential structure). Non-parametric: No fixed number of parameters, no assumptions on data distribution. Versatile: Effectively used for both regression and classification. 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 ... Decision trees are widely used machine learning algorithms and can be applied to both classification and regression tasks. They work by splitting data based on feature values, forming a tree-like structure where each leaf node gives a prediction. Learn what a decision tree is, how it works, and why it is useful for machine learning. This article covers the basic terminology, the algorithm steps, and the attribute selection measures of decision trees.