jarvis.ai.pkgs.lgbm.regression
==============================

.. py:module:: jarvis.ai.pkgs.lgbm.regression

.. autoapi-nested-parse::

   Modules for LightGBM regression.



Attributes
----------

.. autoapisummary::

   jarvis.ai.pkgs.lgbm.regression.default_param_dist


Functions
---------

.. autoapisummary::

   jarvis.ai.pkgs.lgbm.regression.regression
   jarvis.ai.pkgs.lgbm.regression.get_lgbm
   jarvis.ai.pkgs.lgbm.regression.parameters_dict


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

.. py:function:: regression(X=[], Y=[], jid=[], test_size=0.1, plot=False, preprocess=True, feature_importance=True, save_model=False, feat_names=[], model_name='my_model', config={})

   Get generic regression model.


.. py:data:: default_param_dist

.. py:function:: get_lgbm(train_x, val_x, train_y, val_y, cv, n_jobs, scoring, n_iter, objective, alpha, random_state, param_dist=default_param_dist)

   Train a lightgbm model.

   Args:

       train_x: samples used for trainiing

       val_x: validation set

       train_y: train targets

       val_y: validation targets

       cv: # of cross-validations

       n_jobs: for making the job parallel

       scoring: scoring function to use such as MAE

   Returns:
          Best estimator.


.. py:function:: parameters_dict()

   Give example optimized parameters.