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. Hold the experiment constant
Use the same dataset version, preprocessing, split, and metric definition when comparing algorithms.
Verify before continuingThe target, row meaning, and prediction timing are explicit.
2. Read more than one metric
A single score can hide class imbalance, poor calibration, subgroup errors, or unstable residuals.
Verify before continuingTransformations are learned from training rows only and can be reproduced during testing.
3. Include operational cost
Consider inference latency, artifact size, explainability, input requirements, and retraining frequency.
Verify before continuingThe selected metric reflects the cost of errors and is compared on the same holdout.
4. Record the decision
Keep the chosen run and document why it fits the product objective better than alternatives.
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