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. 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. 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 how to use decision trees for classification and regression with scikit-learn, a Python machine learning library. Decision trees are non-parametric models that learn simple decision rules from data features.
