jarvis.ai.uncertainty.lgbm_quantile_uncertainty
===============================================

.. py:module:: jarvis.ai.uncertainty.lgbm_quantile_uncertainty

.. autoapi-nested-parse::

   Code to predict properties and their uncertainty.

   ML model used: lgbm



Functions
---------

.. autoapisummary::

   jarvis.ai.uncertainty.lgbm_quantile_uncertainty.quantile_regr_predint


Module Contents
---------------

.. py:function:: quantile_regr_predint(x, y, jid, cv=2, n_jobs=-1, n_iter=10, random_state=508842607, scoring='neg_mean_absolute_error', prop='exfoliation_energy', write_files=True)

   Perform Quantile regression and determine prediction intervals.

   LOWER_ALPHA = 0.16
   Mid model uses ls as loss function, not quantile, to
       optimize for the mean, not the median
   UPPER_ALPHA = 0.84
   This choice of LOWER_ALPHA, UPPER_ALPHA gives a prediction
   interval ideally equal to 0.68, i.e. 1 standard deviation.
   However, the number of in-bound prediction must be computed
   for the specific fitted models, and that gives the true meaning
   of the uncertainties computed here.
   See:
   https://machinelearningmastery.com/prediction-intervals-for-machine-learning
   https://www.inovex.de/blog/uncertainty-quantification-deep-learning