What is a Decision Tree in machine learning?

A Decision Tree in machine learning is primarily known as a visual representation of decisions and outcomes. This model is structured like a tree, with nodes representing decisions or tests on attributes, branches representing the outcomes of these tests, and leaves representing the final decision or classification outcomes. The hierarchical structure makes it easy to understand and interpret the decision-making process, illustrating how input features lead to specific outputs.

Using a Decision Tree, individuals can see how various decisions are made based on the data provided, making it an effective tool for both classification and regression tasks. The clarity of this model allows users to follow the path of decisions leading to the output, facilitating better insights into how different variables contribute to the final prediction. This characteristic is a significant advantage in contexts where interpretability is crucial.

The other options do not accurately describe a Decision Tree's function in machine learning. For instance, it is not a type of unsupervised learning model, as Decision Trees are typically trained using labeled data in supervised learning contexts. Additionally, while data governance is essential, a Decision Tree is not a framework for governance but rather a tool for analyzing and making decisions based on data. Lastly, it has no relation to data encryption, which is a method of securing data rather than a way to