AutoTrain MLReliable import
Choose the delimiter and verify parsed columns before saving a dataset.
CSV to model
Go from CSV data to a tested model in one workflow
Use a familiar tabular file as the starting point, then keep dataset preparation, model training, testing, and export connected.
Workflow
A practical model workflow
AutoTrain keeps preparation, training, comparison, testing, and export attached to the same project so the experiment can be reopened from another device without rebuilding the setup.
Schema-aware setup
Select the target, handle missing values, encode categorical features, and scale numeric columns when needed.
New-data testing
Enter a row manually or upload a CSV that follows the generated model template.
How it works
1. Upload the CSV
Upload the CSV and confirm its separator.
2. Review data types,
Review data types, missing values, and the target column.
3. Save preprocessing and
Save preprocessing and create a training project.
4. Train, evaluate, then
Train, evaluate, then download the testing template for new rows.
Coverage
Supported tabular tasks
Choose the task from the problem definition. AutoTrain does not silently change the method or hyperparameters selected by the user.
| Task | Typical target | Evaluation focus | Testing output |
|---|---|---|---|
| Classification | Category or class | Accuracy, precision, recall, F1, confusion matrix, ROC/PR where available | Predicted class, confidence, probabilities |
| Regression | Continuous numeric value | MAE, RMSE, R², residual behavior | Predicted numeric value |
| Clustering | No supervised target | Silhouette, cluster size, stability, interpretability | Assigned cluster and distance context |
Saved output
What remains available after training
Experiment record
Dataset reference, preprocessing settings, task, algorithm, hyperparameters, validation metrics, and generated visualizations remain attached to the saved experiment.
Testing interface
Use custom single-row values or a schema-matched CSV to test new data. Results can be exported as CSV for downstream review.
Model artifact
Completed models can be exported with the preprocessing and input schema required to reproduce predictions correctly.
Prediction endpoint
Supported models expose a schema endpoint and a prediction endpoint that can be called from an application backend.
Methodological basis
Automation with explicit controls
AutoML research describes automation across data preparation, model selection, optimization, and evaluation. AutoTrain applies that principle selectively: repetitive execution is automated while task, preprocessing, algorithm, and parameter choices remain visible.
He, X., Zhao, K., & Chu, X. (2021). AutoML: A survey of the state-of-the-art. Knowledge-Based Systems, 212, 106622. Read the journal article
Important: AutoTrain helps execute and document experiments. You remain responsible for data rights, leakage checks, metric selection, bias review, and production monitoring.
FAQ
Common questions
Does AutoTrain choose the best model automatically?
No. It reports comparable experiment results; the user chooses methods, parameters, and the final model based on the actual objective.
Can an experiment be reopened on another device?
Yes. Saved datasets, experiments, model metadata, visual results, and testing tabs are synchronized through AutoTrain Engine.
Is a benchmark result a production guarantee?
No. Benchmarks are fixed reference runs. Real performance depends on data quality, the split strategy, leakage, drift, and the cost of each error type.