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California Housing Example
C
California Housing Example
Developed by quantile-forest
This is a quantile forest-based regression model for predicting California housing prices and providing uncertainty estimates.
Downloads 22
Release Time : 9/14/2024
Model Overview
The model is trained on the California housing dataset, capable of performing quantile regression and generating prediction intervals, suitable for regression tasks requiring uncertainty estimation.
Model Features
Quantile Prediction
Capable of predicting values at different quantiles, not just mean prediction
Uncertainty Estimation
Provides prediction intervals to quantify prediction uncertainty
sklearn Compatible
Compatible with the scikit-learn ecosystem for easy integration into existing workflows
Model Capabilities
Tabular Data Regression
Quantile Prediction
Prediction Interval Generation
Uncertainty Quantification
Use Cases
Real Estate
Housing Price Prediction
Predict median housing prices in different regions of California
R-squared value reaches 0.806
Risk Assessment
Price Volatility Assessment
Assess potential housing price fluctuation ranges
Provides prediction results at different quantiles