Min Max Scaler

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

Input

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

Output

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

Type

ml-estimator

Class

fire.nodes.etl.NodeMinMaxScaler

Fields

Name	Title	Description
inputCol	Input Column	The input column name
outputCol	Output Column	The output column name
max	Max	The upper bound after transformation, shared by all features
min	Min	The lower bound after transformation, shared by all features

Details

Min Max Scaler Transform Node Details

The Min Max Scaler Transform Node is used to rescale a dataset of Vector rows by transforming each feature to a specific range, often [0, 1]. It takes no additional parameter other than the input DataFrame and the output column name.

It rescales each feature individually to a specific range by dividing through the largest minimum-maximum value in each feature and it adds a new column to the DataFrame with the scaled feature values.

Input Parameters

INPUT DATAFRAME: The dataframe containing the feature column to be transformed.

OUTPUT COLUMN : The name of the output column after rescaling.

Examples

Min Max Scaler Transform Node Example

Consider the below Min Max Scaler output for the features column

id features scaled_features

0 [1.0, 2.0, 3.0, 4.0] [0.0, 0.33, 0.67, 1.0]

1 [-1.0, -2.0, -3.0, -4.0] [0.0, 0.33, 0.67, 1.0]

In this example, the input column is features and the output column is scaled_features. The minmax scaler scales the features individually to the range [0,1],by dividing through the largest minimum-maximum value in each feature. Here the minimum value is -4.0 and the maximum value is 4.0 so the features are divided by (4.0 - (-4.0)) = 8.0.