AutoTrain MLInput features
Annual spend across fresh, milk, grocery, frozen, detergents/paper, and delicatessen categories
Machine learning use case
Customer segmentation
Explore unsupervised customer groups with K-Means, agglomerative clustering, and Gaussian mixtures.
Experiment design
Setup used for the reference run
This fixed setup makes the comparison understandable and repeatable. It is an example configuration, not an automatic recommendation for every dataset.
DatasetUCI Wholesale Customers
TargetNo supervised target
Validation80/20 holdout
Random state42
Preprocessing
Exclude Channel and Region from the unsupervised feature set, apply log1p to skewed spend, then standardize.
Build sequence
Recreate the experiment in AutoTrain
- Inspect the dataset.Confirm field types, missing values, class balance, identifiers, and whether each feature exists at prediction time.
- Save preprocessing.Apply the documented transformations without fitting them on the holdout rows.
- Train comparable runs.Keep the dataset version and split constant while changing only the algorithm or intended hyperparameters.
- Review errors.Use task-appropriate metrics and inspect where the model fails before creating a testing tab.
Fixed reference run
Reference benchmark
These values were computed with scikit-learn using the setup above. They show what one reproducible baseline looks like; they are not copied from the cited paper and are not a production guarantee.
Silhouette by model
Higher is betterAll runs use three clusters and random state 42 where applicable.
Reading the result
What the benchmark means
K-Means and agglomerative clustering are nearly tied, and all silhouette values are modest. Treat the clusters as exploratory summaries and validate them against business outcomes before naming or activating them.
Benchmark vs. your experiment: the chart above is a static reference. Results shown inside your dashboard are generated from your own dataset, parameters, validation split, and saved model.
Visual context for the customer segmentation workflow. Model selection should be based on validation metrics and product constraints.
Limitations and risk
Low silhouette values indicate overlapping groups. Segment names require business interpretation and must be validated against downstream outcomes.
Before deployment
Repeat validation on recent, representative data; review subgroup errors; define a fallback; and monitor input and outcome drift.
Human decisions
Use the prediction as evidence, not as the sole decision maker, where an error could affect education, health, access, pricing, or customer treatment.
Journal foundation
Punj & Stewart (1983), Cluster Analysis in Marketing Research: Review and Suggestions for Application, Journal of Marketing Research.
The journal reference provides methodological or domain context. AutoTrain's benchmark above is a separate implementation using the stated holdout configuration.
Open journal article