Decision tree algorithm: Decision trees are widely

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. The Decision Tree Algorithm is a powerful tool in machine learning, used for classification and regression tasks. This article explains its working, types, advantages, limitations, and applications. Learn how to implement it in Python with a practical example. 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. 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 ...