TD_RowNormalizeTransform Function | RowNormalizeTransform - TD_RowNormalizeTransform - Teradata Vantage

Teradata® VantageCloud Lake

Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
Language
English (United States)
Last Update
2024-04-03
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TD_RowNormalizeTransform normalizes input columns row-wise, using TD_RowNormalizeFit output.

Row normalization is a technique to transform a matrix or a dataset so that each row has the same magnitude or scale. This is typically done to make it easier to compare rows and to avoid bias towards variables with higher values.

Suppose you have a table that represents the daily sales figures for a retail store over a period of five days.

Day 1 Day 2 Day 3 Day 4 Day 5
120 150 80 200 90
90 110 100 120 130
200 180 150 170 190

One method of normalization is to divide each element in a row by the sum of all the elements in that row.

Day 1 Day 2 Day 3 Day 4 Day 5
120/640 150/640 80/640 200/640 90/640
90/550 110/550 100/550 120/550 130/550
200/890 180/890 150/890 170/890 190/890

The result is a table where each row has a sum of 1, representing a proportion or percentage of the total sales for each day.

Day 1 Day 2 Day 3 Day 4 Day 5
0.1875 0.2344 0.125 0.3125 0.1406
0.2079 0.2539 0.2308 0.2771 0.3001
0.2247 0.2022 0.1685 0.1910 0.2135

Each row contains normalized values for total sales for each day, making it easier to compare the performance of the store on different days using a machine learning pipeline because all values have the same impact and magnitude.