Start from a decision question, inspect a fixed reference setup, read the benchmark carefully, and continue into the matching dataset and AutoTrain guide.
These are educational reference experiments. Your dashboard result is generated independently from your own dataset and settings.
Build and compare customer churn classifiers using contract, service, tenure, and billing features.
Compare regression models for median house value prediction using the California Housing dataset.
Model final student grades with demographic, social, school, and earlier-grade features.
Compare multiclass models for obesity-level prediction from eating habits and physical condition features.
Explore unsupervised customer groups with K-Means, agglomerative clustering, and Gaussian mixtures.
Use the examples to understand experiment structure. Choose metrics, validation, and thresholds from the real decision cost and the data available at prediction time.
AutoTrain ML