CompTIA DataX (DY0-001) — Question 76

A data scientist would like to model a complex phenomenon using a large data set composed of categorical, discrete, and continuous variables. After completing exploratory data analysis, the data scientist is reasonably certain that no linear relationship exists between the predictors and the target. Although the phenomenon is complex, the data scientist still wants to maintain the highest possible degree of interpretability in the final model. Which of the following algorithms best meets this objective?

Answer options

• A. Artificial neural network
• B. Decision tree
• C. Multiple linear regression
• D. Random forest

Correct answer: B

Explanation

The Decision tree algorithm is the best choice for maintaining interpretability, as it provides a clear visual representation of decision rules. In contrast, Artificial neural networks and Random forests are more complex and difficult to interpret, while Multiple linear regression assumes a linear relationship, which the data scientist has already ruled out.