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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 25·5 min read
Correcting Variable Importance Scored by Random Forests
TL;DR
Random Forests' variable importance scores often misrepresent the significance of correlated variables. This study introduces methods to correct these scores by grouping variables based on their conditional correlations.
✦ Why It Matters
Implement these variable grouping methods to improve the accuracy of your Random Forests model interpretations today.
Key Takeaways
- Use conditional correlations to group variables for better importance scores.
- Isolate variables of interest from correlated peers in analysis.
- Apply clustering techniques to enhance variable importance assessments.
- Improve model diagnostics and decision-making with corrected scores.
How It Works
The proposed methods involve grouping variables based on their conditional correlations with the response variable. The first method isolates the variable of interest by grouping correlated variables individually, while the second method uses clustering to create groups based on pair-wise correlations.
This approach ensures that the influence of correlated variables does not mask the importance of the variable being analyzed.