Member-only story
What are some pros and cons of decision trees?
Learn Pros and Cons of Decision Trees for a Path to High-Paying AI Jobs: Key Interview Questions and Expert Answers
This article is the series of Path to a High-Paying AI Jobs: Key Interview Questions and Expert Answers Index Article. You can find Key Interview Questions that are highly asked on High-Paying AI Job Interviews and links to expert answer articles from that page.
Decision trees are a popular machine learning algorithm used for both classification and regression tasks. They work by recursively splitting the dataset into smaller subsets based on the most significant features, forming a tree-like structure where each internal node represents a decision based on a feature, and each leaf node represents an outcome. Decision trees are widely used because of their interpretability and simplicity, but like any algorithm, they come with strengths and weaknesses.
Pros of Decision Trees:
- Interpretability and Transparency:
- Pro: Decision trees are easy to understand and interpret, even for non-technical stakeholders. The model’s decisions can be visualized in a tree structure, making it straightforward to trace how the model arrived at a particular prediction.
