The primary requirements are to model a non-linear phenomenon and maintain the highest possible degree of interpretability. A decision tree is the best choice as it naturally handles non-linear relationships by partitioning the feature space into distinct regions. Its hierarchical, rule-based structure (a series of if-then-else conditions) is highly intuitive and easy to visualize and explain to stakeholders. While other models can handle complexity, they sacrifice this crucial interpretability. A decision tree provides a clear, traceable path for each prediction, directly fulfilling the data scientist's main objective.

A. Artificial neural network: Fails the primary requirement for high interpretability; these are complex "black box" models whose internal logic is difficult to decipher.

C. Multiple linear regression: Is fundamentally unsuitable because the data scientist has already determined that no linear relationship exists between the predictors and the target.

D. Random forest: Is an ensemble of many decision trees, which significantly reduces interpretability compared to a single, well-defined tree.

1. James

G.

Witten

D.

Hastie

T.

& Tibshirani

R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer. In Chapter 8

Section 8.1

"The Basics of Decision Trees

" the authors state

"Trees are very easy to explain to people... Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches... The cost of a tree—that is

its complexity and interpretability—is governed by the number of splits it has." This highlights the direct link between a single tree and high interpretability.

2. Molnar

C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. In Chapter 4.1

"Decision Tree

" it is noted

"The decision tree is one of the most interpretable models... The 'if-then' structure is intuitive for humans." The chapter contrasts this with the lack of interpretability in more complex models like neural networks and the reduced transparency of ensembles like random forests.

3. MIT OpenCourseWare. (2020). 6.036 Introduction to Machine Learning

Fall 2020. Lecture 9: Decision Trees. The course materials describe decision trees as a non-linear model class that is "very easy to interpret" by following the path from the root to a leaf node

which directly corresponds to a set of rules. This is contrasted with ensembles

which gain predictive power at the cost of this direct interpretability.