MaxAbs Scaler Transform
===========

Rescale each feature individually to range [-1, 1] by dividing through the largest maximum absolute value in each feature.

Type
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ml-predict

Class
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fire.nodes.etl.NodeMaxAbsScalerTransform

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

      * - Name
        - Title
        - Description


Details
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MaxAbs Scaler Transform Node Details
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The MaxAbs Scaler Transform Node is used to rescale a dataset of Vector rows individually feature-wise to the given range [-1, 1]. It rescales each feature individually by dividing through the largest maximum absolute value in each feature. It also takes in a fit model as input, which is typically the output of a previous MaxAbs Scaler Estimator Node.

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



Input Parameters
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FIT MODEL : The output of a previous MaxAbs Scaler Estimator Node, which contains the maximum absolute value for the transformation.


Examples
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MaxAbs 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 MaxAbs Scaler Estimator Node to specify the maximum absolute value for the transformation, creating a fit model. Then, we use the MaxAbs 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] [0.25, 0.5, 0.75, 1.0]

2 [-1.0, -2.0, -3.0, -4.0] [-0.25, -0.5, -0.75, -1.0]


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