machine_learning.scoring_functions
==================================

.. py:module:: machine_learning.scoring_functions


Functions
---------

.. autoapisummary::

   machine_learning.scoring_functions.mae
   machine_learning.scoring_functions.manual_accuracy
   machine_learning.scoring_functions.mbd
   machine_learning.scoring_functions.mse
   machine_learning.scoring_functions.rmse
   machine_learning.scoring_functions.rmsle


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

.. py:function:: mae(predict, actual)

   Examples(rounded for precision):
   >>> actual = [1,2,3];predict = [1,4,3]
   >>> float(np.around(mae(predict,actual),decimals = 2))
   0.67

   >>> actual = [1,1,1];predict = [1,1,1]
   >>> float(mae(predict,actual))
   0.0


.. py:function:: manual_accuracy(predict, actual)

.. py:function:: mbd(predict, actual)

   This value is Negative, if the model underpredicts,
   positive, if it overpredicts.

   Example(rounded for precision):

   Here the model overpredicts
   >>> actual = [1,2,3];predict = [2,3,4]
   >>> float(np.around(mbd(predict,actual),decimals = 2))
   50.0

   Here the model underpredicts
   >>> actual = [1,2,3];predict = [0,1,1]
   >>> float(np.around(mbd(predict,actual),decimals = 2))
   -66.67


.. py:function:: mse(predict, actual)

   Examples(rounded for precision):
   >>> actual = [1,2,3];predict = [1,4,3]
   >>> float(np.around(mse(predict,actual),decimals = 2))
   1.33

   >>> actual = [1,1,1];predict = [1,1,1]
   >>> float(mse(predict,actual))
   0.0


.. py:function:: rmse(predict, actual)

   Examples(rounded for precision):
   >>> actual = [1,2,3];predict = [1,4,3]
   >>> float(np.around(rmse(predict,actual),decimals = 2))
   1.15

   >>> actual = [1,1,1];predict = [1,1,1]
   >>> float(rmse(predict,actual))
   0.0


.. py:function:: rmsle(predict, actual)

   Examples(rounded for precision):
   >>> float(np.around(rmsle(predict=[10, 2, 30], actual=[10, 10, 30]), decimals=2))
   0.75

   >>> float(rmsle(predict=[1, 1, 1], actual=[1, 1, 1]))
   0.0