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<< Click to Display Table of Contents >> Normalization |
  
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<< Click to Display Table of Contents >> Normalization |
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Normalization is mainly used to eliminate the influence of unit and scale differences between features.
How-To-Use:
Standardization includes two processing: normalization and standardization.
Normalization:
(1) Change the data to a decimal between (0,1) or (1,-1). It is mainly proposed for the convenience of data processing. It is more convenient and faster to map the data to the range of 0 to 1.
(2) Turn the dimensional expression into a non-dimensional expression, so that indicators of different units or magnitudes can be compared and weighted.
Normalization is a way to simplify calculations, that is, a dimensional expression is transformed into a non-dimensional expression and becomes a scalar; Standardization: In machine learning, we may have to deal with different types of data, such as the pixel values on audio and pictures. These data may be high-dimensional. After data standardization, the average value of each feature becomes 0 (the value of each feature is subtracted from the average of the feature in the original data), and the standard deviation becomes 1. This method is widely used in many machine learning Algorithm (For example: support vector machine, logistic regression and MLP);
Precautions: The left ear of the Normalization node can be connected to the data node, and the data of the data node can be standardized. The right ear can be connected to the data set view node, and the standardized result can be viewed in the data set view node. The right ear can also be connected to other operator nodes for subsequent calculations.
❖Configuration
After adding the Normalization node to the experiment, you can set the data Normalization through the "Configuration" page on the right.
[Method] There are three methods of column standardization: StandardScaler, MinMaxScaler, MaxAbsScaler.
[Operation column] Select the column to be standardized. Select columns in the select data pop-up window, and the output result will be all columns (the selected columns will be processed to obtain standardized data).
After setting column standardization, you can view the standardization results by connecting to the data set view's exploration data function; you can also connect to other operator nodes for subsequent calculations.
Normalization right-click menu
❖Run Normalization Node
Run the node, pass the data to DM-Engine for calculation, and get the output result.
❖Reset Normalization Node
The node that has been running is reset, the returned result is deleted, and the node status is changed to not running.
❖Rename Normalization Node
In the right-click menu of the Normalization node, select "Rename" to rename the node.
❖Refresh Normalization Node
In the right-click menu of the Normalization node, select "Refresh" to update the synchronization data or parameter information.
❖Save as Composite Node
In the right-click menu of the Normalization node, select "Save as Composite Node",The selected node can be saved as a composite node to realize a multiplexing node, and the parameters of the saved node are consistent with the original node.
❖Cut Normalization Node
In the right-click menu of the Normalization node, select "Cut" to realize node cutting operation.
❖Copy Normalization Node
In the right-click menu of the Normalization node, select "Copy" to realize node replication operation.
❖Delete Normalization Node
In the right-click menu of a Normalization node, select "Delete" or click the delete key on the keyboard to delete the node and its input and output connections.