Use clustering as an alternative when correlation is weak
ACE-4 Order: #4 Explore Data and Find Signal Has Dependencies
Updated 1 month, 2 weeks ago
Guidance
When linear correlation is weak or absent but you suspect structure, scale the features (sklearn.preprocessing.scale()), run k-means (KMeans(n_clusters=k), k chosen by judgment), and assign human-meaningful labels to the resulting clusters — cluster IDs alone mean nothing.
Details
- Order:
- #4
- Phase:
- Predecessor:
- ACE-3 Evaluate model quality
- Successor:
- ACE-5 Rank features by information gain and draw conclusions
- 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
- Unsupervised Clustering Predictive/Exploratory Modeling scikit-learn
Rules
-
k is an analyst's judgment call
k-is-an-analysts-judgment-call -
Scale variables before clustering
scale-variables-before-clustering
Input Artifacts
No input artifacts
Output Artifacts
No output artifacts