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license: cc-by-4.0
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These are Quantile Random Forest Regression models trained to predict the kpoints-density with different confidence levels: 
1. QRF95.pkl predicts (0.05, 0.5, 0.95) quantiles
2. QRF90.pkl predicts (0.1, 0.5, 0.9) quantiles
3. QRF85.pkl predicts (0.15, 0.5, 0.85) quantiles

The performance of models measured for the 0.5 quantile is:

MAE: 0.064,

MAPE: 0.179, 

MSE: 0.0098, 

R2_score: 0.694, 

Spearman_corr: 0.862, 

Kendall_corr: 0.682

Models are trained on the dataset generated for this work.

Associated GitHub repositories:

https://github.com/stfc/goldilocks

https://github.com/stfc/goldilocks_kpoints