AutoTrain MLBefore you begin: use a dataset you are allowed to process, keep a holdout that reflects the real prediction setting, and write down the decision the model is intended to support.
1. Select meaningful features
Exclude IDs and fields that encode an existing label unless they are intentionally part of the segmentation.
Verify before continuingThe target, row meaning, and prediction timing are explicit.
2. Transform skewed data
Log-transform highly skewed non-negative measures and standardize features so scale does not dominate distance.
Verify before continuingTransformations are learned from training rows only and can be reproduced during testing.
3. Compare structures
Try multiple cluster counts and algorithms. Silhouette is useful, but stability and business interpretability also matter.
Verify before continuingThe selected metric reflects the cost of errors and is compared on the same holdout.
4. Name clusters last
Inspect feature distributions after fitting. Cluster labels are arbitrary and should be assigned only after evidence-based interpretation.
Verify before continuingRepresentative new inputs produce valid outputs and failures are handled clearly.
Final checklist
- Dataset version and intended use are documented.
- Preprocessing and validation settings match the saved experiment.
- Metrics are interpreted with class balance, error cost, and limitations in mind.
- New-data testing succeeds with the exact production schema.
- A fallback and monitoring plan exist before deployment.
Next step
Open the dashboard to apply this guide to a saved dataset and experiment.
Open dashboard