Rank features by information gain and draw conclusions
ACE-5 Order: #5 Explore Data and Find Signal Has Dependencies
Updated 1 month, 2 weeks ago
Guidance
Fit a Random Forest Regressor on the feature table against Y to get a feature-importance ranking (an ensemble-of-trees estimate of how much each feature actually informs Y), cross-check it against the regression coefficients and correlations from earlier steps, and write the surviving, validated findings as the phase's Conclusions.
Details
- Order:
- #5
- Phase:
- Predecessor:
- ACE-4 Use clustering as an alternative when correlation is weak
- Created:
- Sep 18, 2026
- Last Updated:
- Sep 18, 2026
Workflow
Assess Information Gain and Conclude
Rigorously test whether the feature table actually carries information about Y — via significance-tested correlation, a validated predictive experiment, and …
View WorkflowAssigned Agent
EDA Practitioner
The person or AI system running this playbook end to end — analyst, programmer, and …
Required Skills
- Ensemble Modeling and Feature Importance Predictive Modeling scikit-learn
Rules
-
Validate regression by comparing train vs. test fit
validate-regression-by-comparing-train-vs-test-fit
Input Artifacts 2
-
Feature Table
Document
Required
Produced by: Engineer and derive features -
Model Validation Report
Document
Required
Produced by: Evaluate model quality
Output Artifacts 1
- Conclusions Summary Document Required