AI Engineering Degree 2025 – 400 Free Practice Questions to Pass the Exam

One of the key advantages of using decision trees is that they provide clear visual representations of decisions. Decision trees operate by splitting the data into subsets based on feature values and decisions at each node, which can be graphically represented in a tree-like structure. This visual representation makes it easy to understand the path through the model and the logic behind predictions.

The clarity offered by decision trees allows stakeholders, including those without a deep statistical background, to interpret and validate the model's decisions easily. This transparency is especially useful in fields where explainability is critical, such as healthcare, finance, and law.

In contrast, the other options present either misconceptions about decision trees or aspects that do not highlight their strengths. For example, decision trees typically require less tuning compared to some other complex models, they have limitations in handling data balance depending on the situation, and they can actually handle both binary and multi-class classifications, providing further versatility.