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 Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation [[1]](#1). The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn forest regressors.
 
 #### Example of fitted model predictions and prediction intervals on California housing data ([code](https://zillow.github.io/quantile-forest/gallery/plot_quantile_intervals.html))
-<img src="https://zillow.github.io/quantile-forest/_static/plot_quantile_intervals.png"/>
+<img src="https://zillow.github.io/quantile-forest/_static/plot_qrf_prediction_intervals.png"/>
 
 Quick Start
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