One Hot Encoder
===========

Maps a column of label indices to a column of binary vectors, with at most a single one-value

Input
--------------
It takes in a DataFrame and transforms it to another DataFrame

Output
--------------
The output DataFrame contains a new column which contains the mapping of a column of label indices to a column of binary vectors, with at most a single one-value.

Type
--------- 

ml-transformer

Class
--------- 

fire.nodes.ml.NodeOneHotEncoder

Fields
--------- 

.. list-table::
      :widths: 10 5 10
      :header-rows: 1

      * - Name
        - Title
        - Description
      * - inputCols
        - Input Columns
        - Input columns for encoding
      * - outputCols
        - Output Columns
        - Output columns


Details
-------
One Hot Encoder Node Details
+++++++++++++++


One-hot encoding is a process by which categorical data (such as nominal data) are converted into numerical features of a dataset. This is often a required preprocessing step since machine learning models require numerical data.


One Hot Encoder Node maps a column of label indices to a column of binary vectors, with at most a single one-value.This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features.



Input Parameters
+++++++++++++++


* OUTPUT STORAGE LEVEL : Keep this as DEFAULT.
* VARIABLES : Add the columns to be one-hot encoded
* Input Columns : Select the categorical field in the input schema for one-hot encoding.
* Output Columns : Set the name of the field in the output schema that contains the binary vector result.


Examples
-------
One Hot Encoder Node Example
+++++++++++++++


Assume that we have the following DataFrame with column v1 as integer data type:

::

    v1 |
    ---|
    1  |
    2  |
    3  |
    4  |
    5  |


The output dataframe that will be obtained by selecting **v1** as the Input columns and **vecOut** as the Output column name will be :


::

     v1 |     vecOut    |
    ----|---------------|
     1  | (5,[1],[1.0]) |
     2  | (5,[2],[1.0]) |
     3  | (5,[3],[1.0]) |
     4  | (5,[4],[1.0]) |
     5  | (5,[],[])     |