DY0-001 · Question #33
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…
The correct answer is B. Decision tree. Decision trees capture complex, nonlinear relationships with a transparent, rule-based structure. They remain highly interpretable (each split can be visualized and explained) unlike ensembles (random forests) or neural networks, and they don't rely on linear assumptions.
Question
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?
Options
- AArtificial neural network
- BDecision tree
- CMultiple linear regression
- DRandom forest
How the community answered
(60 responses)- A17% (10)
- B47% (28)
- C28% (17)
- D8% (5)
Explanation
Decision trees capture complex, nonlinear relationships with a transparent, rule-based structure. They remain highly interpretable (each split can be visualized and explained) unlike ensembles (random forests) or neural networks, and they don't rely on linear assumptions.
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