Refer to the feature importance graph below.

If you want to simplify the model with minimal loss in predictive power, which features could you REMOVE so that the remaining features still account for at least 80% of the total importance? [2 marks]

Question

Refer to the feature importance graph below.

If you want to simplify the model with minimal loss in predictive power, which features could you REMOVE so that the remaining features still account for at least 80% of the total importance? [2 marks]

BlackTom AI · Accepted Answer

The task presents a question about feature importance and removing features to keep at least 80% of the total importance, but there is no list of answer options to analyze.
First, restating the core prompt: the question asks which features could be removed so that the remaining features still account for at least 80% of the total importance, based on a feature importance graph, with the goal of simplifying the model with minimal loss in predictive power.
Because the answer_options array is empty, we cannot evaluate multiple candidate choices or compare their merits. In a typical scenario, you would examine the ranked features by importance, identify the smallest set of features whose cumulative importance reaches or exceeds 80%, and then declare that all other features (the ones not in that top set) could be removed without dropping below the 80% threshold.
What you can reason from the provided information (without explicit options):
- The correct approach is to perform a cumulative-sum check on the sorted-by-importance features, starting from the most important feature and adding features until the sum meets or exceeds 80% of the total importance.
- Any feature not included in that minimal top set would be considered for removal since its contribution would be part of the remaining 20% (or less) of total importance.
- This strategy aligns with the idea of keeping the most informative features while discarding those with the smallest impact on model performance, thereby simplifying the model with minimal predictive loss.
Potential caveats to consider when applying this in practice:
- If several features have nearly identical importance scores around the 80% cutoff, you might consider domain knowledge or cross-validation to decide which among them to keep or drop.
- The 80% threshold is a heuristic; in some datasets, you may tolerate a different threshold (e.g., 75% or 90%) depending on the desired balance between simplicity and accuracy.
- Be mindful of feature interactions: removing features individually based on marginal importance may overlook interaction effects where a feature is more informative in combination with others.
- If the graph includes negative importance values (in some models or feature-engineering setups), interpretations may differ; typically, feature importance is nonnegative, but you should verify how the graph handles such cases.
- Always validate the impact of removal with a model re-evaluation (e.g., cross-validated accuracy or AUC) to ensure the practical loss in predictive power remains within acceptable limits.
Since there are no explicit options to evaluate, this analysis describes the correct reasoning pattern and the key steps you would follow to determine which features to remove given a feature importance graph and an 80% cumulative-importance goal.

Refer to the feature importance graph below. If you want to simplify the model with minimal loss in predictive power, which features could you REMOVE so that the remaining features still account for at least 80% of the total importance? [2 marks]

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