Who leads the California Housing benchmark?

XGBoost currently leads California Housing with a score of 0.45 on Rmse.

What is the state-of-the-art score on California Housing?

The state-of-the-art result on California Housing is 0.45 (Rmse), achieved by XGBoost as of 2026.

How many models are tracked on California Housing?

Codesota tracks 2 models on California Housing.

When was the California Housing leaderboard last updated?

The California Housing leaderboard on Codesota includes results through 2026.

Codesota · Benchmark · California HousingHome/Leaderboards/Structured Data & Forecasting/Tabular Regression/California Housing

Unknown

California Housing.

Name: California Housing Benchmark Results
Creator: Unknown
Published: 2026-01-01
License: https://creativecommons.org/licenses/by/4.0/

Predict median house values from California census data

Paper ↗Leaderboard ↓

§ 01 · Leaderboard

Results by metric.

Only 2 models on this benchmark

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Found a wrong score or missing run?

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Rmse

Rmse is the reported evaluation metric for California Housing. Codesota tracks published model scores on this metric so readers can compare state-of-the-art results across sources and model families.

Higher is better

Trust tiers for Rmseverifiedpapervendorcommunityunverified

Rank	Model	Trust	Score	Year	Links	Fix
01	XGBoost RMSE computed from MSE=0.2050 on standard 80/20 scikit-learn split. Target in $100k units.	verified	0.45	2026	Source ↗	Looks wrong?
02	LightGBM LightGBM RMSE=0.4333 on standard scikit-learn California Housing split. Target in $100k units.	verified	0.43	2026	Source ↗	Looks wrong?

§ 04 · Submit a result

Add to the leaderboard.

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