jarvis.ai.pkgs.sklearn.classification
=====================================

.. py:module:: jarvis.ai.pkgs.sklearn.classification

.. autoapi-nested-parse::

   Simple ML models for classifcation and regression.

   Designed for educational purposes only



Attributes
----------

.. autoapisummary::

   jarvis.ai.pkgs.sklearn.classification.simple_class_models


Functions
---------

.. autoapisummary::

   jarvis.ai.pkgs.sklearn.classification.classify_roc_ml
   jarvis.ai.pkgs.sklearn.classification.classification


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

.. py:data:: simple_class_models

.. py:function:: classify_roc_ml(X=[], y=[], classes=[0, 1, 2], names=['High val', 'Low val', ''], n_plot=1, method='', preprocess=True, plot=False, test_size=0.1)

   Classifcation module for ROC curve for upto three classes.

   It can be expanded in more classes as well.
   Args:
       X: input feature vectors

       y: target data obtained from binary_class_dat

       classes: dummy classes

       names: name holders for the target data

       method: ML method

       preprocess: whether to apply standard preprocessing techniques

       plot: whether to plot the ROC curve


.. py:function:: classification(X=[], Y=[], tol=100, plot=False, preprocess=True, models=simple_class_models, model_name='my_model', save_model=False)

   Quickly train some of the classifcation algorithms in scikit-learn.