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. Prepare the dataset
Use one row per observation. Keep the target out of feature transformations, remove identifiers that leak the outcome, and inspect class balance before training.
Verify before continuingThe target, row meaning, and prediction timing are explicit.
2. Configure preprocessing
Handle missing values, one-hot encode nominal categories, and scale numeric features when the selected estimator benefits from scaling.
Verify before continuingTransformations are learned from training rows only and can be reproduced during testing.
3. Evaluate
Review accuracy together with precision, recall, F1, confusion matrix, ROC, and precision-recall output. Pick metrics based on the cost of each error type.
Verify before continuingThe selected metric reflects the cost of errors and is compared on the same holdout.
4. Test new rows
Create a Model Testing tab. Enter feature values manually or download the CSV template, add new rows, upload it, and export predictions.
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