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

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,[],[])     |