Standard Scaler Transform
===========

StandardScaler transforms a dataset of Vector rows, normalizing each feature to have unit standard deviation and/or zero mean.

Input
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It takes in a DataFrame as input and transforms it to another DataFrame

Output
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It adds a new column containing the transform of the input Vector column to unit standard deviation and/or zero mean features to the incoming DataFrame.

Type
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ml-predict

Class
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fire.nodes.ml.NodeStandardScalerTransform

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

      * - Name
        - Title
        - Description


Details
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Standard Scaler Transform Node Details
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The Standard Scaler Transform Node is used to normalize a dataset of Vector rows, by transforming each feature to have unit standard deviation and/or zero mean. It takes in an input DataFrame and transforms it to another DataFrame. It also takes in a fit model as input, which is typically the output of a previous Standard Scaler Estimator Node.

The transformed DataFrame contains a new column with the transformed features.



Input Parameters
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FIT MODEL : The output of a previous Standard Scaler Estimator Node, which contains the specifications for the transformation.


Examples
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Standard Scaler Transform Node Example
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Consider the following example, where we have a DataFrame with a column 'features' containing continuous values. We use a Standard Scaler Estimator Node to specify the transformation, creating a fit model. Then, we use the Standard Scaler Transform Node to transform the 'features' column using the fit model.


Input DataFrame:


id features

1 [1.0, 2.0, 3.0, 4.0]

2 [-1.0, -2.0, -3.0, -4.0]


Output


id features scaled_features

1 [1.0, 2.0, 3.0, 4.0] [-1.34164079, -0.4472136, 0.4472136, 1.34164079]

2 [-1.0, -2.0, -3.0, -4.0] [-1.34164079, -1.34164079, -1.34164079, -1.34164079]

In this example, the input column is 'features' and the output column is 'scaled_features'. The fit model is created using the Standard Scaler Estimator Node. The Standard Scaler Transform Node uses this fit model to transform the 'features' column, creating a new column 'scaled_features' with the transformed features.