Artificial Intelligence Governance Professional AIGP · Free Practice Question Medium
Question 32
A pharmaceutical company is using a random forest to predict patient responses to a new medication. What best practices should the data science team follow to maximize the effectiveness of their model?
- A Assess feature importance as calculated by the random forest to gain insights into predictors
- B Tune the number of trees and maximum depth to balance performance and computation
- C Combine outputs from all trees using a simple majority vote (for classification) or average (for regression)
- D Rely on a single decision tree for all predictions, as it is easier to interpret
Reveal correct answers
Correct answers: A, B, C
Explanation
Tuning parameters, analyzing feature importance, and aggregating tree outputs are vital; relying on a single tree ignores the strengths of random forests.A. Random forests provide feature importance scores, useful for understanding what drives predictions.
B. Optimal parameter settings reduce overfitting and manage training time.
C. Ensemble aggregation is central to random forest's improved accuracy.
D. Single trees are less accurate and more prone to overfitting than ensembles.
Discussion
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