.. _`Imputer`:

.. _`org.sysess.sympathy.machinelearning.imputer`:

Imputer
=======

.. image:: imputer.svg
   :width: 48


Replaces missing values in a data set with a computed value infered from the remained of the data set. If there are missing data in the data set, those needs to be removed or replaced first.



**Documentation**

Replaces missing values in a data set with a computed value infered from the remained of the data set. If there are missing data in the data set, those needs to be removed or replaced first.

*Configuration*:


  - *missing_values*

    The placeholder for the missing values. All occurrences of
    `missing_values` will be imputed.


  - *strategy*

    The imputation strategy.

    - If "mean", then replace missing values using the mean along
      each column. Can only be used with numeric data.
    - If "median", then replace missing values using the median along
      each column. Can only be used with numeric data.
    - If "most_frequent", then replace missing using the most frequent
      value along each column. Can be used with strings or numeric data.
    - If "constant", then replace missing values with fill_value. Can be
      used with strings or numeric data.

    .. versionadded:: 0.20
       strategy="constant" for fixed value imputation.




*Attributes*:


  - *statistics_*

    The imputation fill value for each feature.
    Computing statistics can result in `np.nan` values.
    During :meth:`transform`, features corresponding to `np.nan`
    statistics will be discarded.




*Input ports*:


*Output ports*:
    **model** : model
        Model


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_preprocessing

.. class:: Imputer