AutoTrain MLInput features
8 numeric census and geographic features
Machine learning use case
House price prediction
Compare regression models for median house value prediction using the California Housing dataset.
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.
DatasetCalifornia Housing
TargetMedian house value
Validation80/20 holdout
Random state42
Preprocessing
Check geographic leakage, split before fitting transformations, and scale when required by the selected estimator.
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.
R² by model
Higher is betterMAE in $100,000 units: 0.533, 0.328, and 0.372.
Reading the result
What the benchmark means
Random Forest gives the highest R² and lowest MAE in this holdout. The result is useful as a nonlinear baseline, but geographic grouping and temporal drift need separate validation before real valuation work.
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 house price prediction workflow. Model selection should be based on validation metrics and product constraints.
Limitations and risk
The data represents 1990 California census blocks, not current individual properties. Geography and market drift limit direct production use.
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
Pace & Barry (1997), Sparse spatial autoregressions, Statistics & Probability Letters.
The journal reference provides methodological or domain context. AutoTrain's benchmark above is a separate implementation using the stated holdout configuration.
Open journal article