Min Max Scaler Transform
===========

MinMaxScaler transforms a dataset of Vector rows, rescaling each feature to a specific range (often [0, 1])

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

Output
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A new column containing the scaled features is added to the incoming DataFrame

Type
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ml-predict

Class
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fire.nodes.etl.NodeMinMaxScalerTransform

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

      * - Name
        - Title
        - Description


Details
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Min Max Scaler Transform Node Details
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The Min Max Scaler Transform Node is used to transform a dataset of Vector rows, rescaling each feature to a specific range, often [0, 1]. 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 Min Max 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 Min Max Scaler Estimator Node, which contains the specified range for the transformation.


Examples
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Min Max 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 Min Max Scaler Estimator Node to specify the range for the transformation, creating a fit model. Then, we use the Min Max 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] [0.0, 0.5, 1.0, 1.5]

2 [-1.0, -2.0, -3.0, -4.0] [-1.5, -1.0, -0.5, 0.0]

In this example, the input column is 'features' and the output column is 'scaled_features'. The fit model is created using the Min Max Scaler Estimator Node with the specified range of [0,1]. The Min Max Scaler Transform Node uses this fit model to transform the 'features' column, creating a new column 'scaled_features' with the scaled features.