AutoTrain MLInput features
Eating habits, physical condition, age, gender, height, and weight
Machine learning use case
Obesity risk classification
Compare multiclass models for obesity-level prediction from eating habits and physical condition features.
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 Obesity Levels
TargetNObeyesdad (7 classes)
Validation80/20 holdout
Random state42
Preprocessing
One-hot encode categories, standardize numeric variables, and stratify the train/test split.
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.
Accuracy by model
Higher is betterMacro F1: 0.871, 0.936, and 0.950.
Reading the result
What the benchmark means
Gradient Boosting leads this fixed split on accuracy and macro F1. Because most rows are synthetic, this demonstrates multiclass modeling rather than clinical validity.
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 obesity risk classification workflow. Model selection should be based on validation metrics and product constraints.
Limitations and risk
The source reports that 77% of records are synthetic. This benchmark is educational and must not be treated as medical diagnosis or clinical guidance.
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
Palechor & de la Hoz Manotas (2019), Dataset for estimation of obesity levels based on eating habits and physical condition, Data in Brief.
The journal reference provides methodological or domain context. AutoTrain's benchmark above is a separate implementation using the stated holdout configuration.
Open journal article