Codesota · Time Series · Tabular Regression · California HousingTasks/Time Series/Tabular Regression
Tabular Regression · benchmark dataset · 1997 · EN

California Housing.

Predict median house values from California census data

Submit a result ↵
§ 01 · Leaderboard

Best published scores.

2 results indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.

Primary
rmse · higher is better
rmse· primary
2 rows
#ModelOrgSubmittedPaper / codermse
01XGBoostOSSDMLCMar 2026xgboosting.com reference0.453
02LightGBMOSSMicrosoftMar 2026LightGBM scikit-learn benchmark0.433
Fig 2 · Rows sorted by score within each metric. Shaded row marks SOTA. Dates reflect model or paper release where available, otherwise the date Codesota accessed the source.
§ 03 · Progress

1 steps
of state of the art.

Each row below marks a model that broke the previous record on rmse. Intermediate submissions are kept in the leaderboard above; only SOTA-setting entries are re-listed here.

Higher scores win. Each subsequent entry improved upon the previous best.

SOTA line · rmse
  1. Mar 28, 2026XGBoostDMLC0.453
Fig 3 · SOTA-setting models only. 1 entries span Mar 2026 → Mar 2026.
§ 06 · Contribute

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

Submit a result ↵Read submission guide
What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies