TD_RowNormalizeFit outputs a table of parameters and specified input columns to input to TD_RowNormalizeTransform, which normalizes the input columns row-wise.
Normalization is a data preprocessing technique used in machine learning to scale data to a common range. It is a process of transforming numerical data to a standardized format, ensuring that no variable dominates the others.
Row normalization is used to scale the values in each row of a dataset to a specific norm or range. This ensures that all rows have equal importance in the analysis and reduces the influence of the magnitude of the features on the results.
- Comparing variables: Compare variables that are measured on different scales. By normalizing the rows, variables with different scales can be compared on a common scale.
- Data preprocessing: Pre-processing machine learning pipelines to improve the performance of machine learning models. Normalizing the rows can make the data more adaptable for clustering, classification, and other machine learning algorithms.
- Interpretation: Improve interpretation of the data. By normalizing the rows, the data can be transformed into a standard format that is easier to understand and analyze.
- Outlier handling: Handle outliers. Outliers can skew the data and make it difficult to identify patterns. Normalizing the rows can help to reduce the impact of outliers by scaling the values to a standard range.
Row normalization is particularly useful when dealing with datasets that have features with significantly different scales or ranges. By normalizing the rows, the algorithm can effectively compare the similarity or distance between the rows based on the direction of the feature vectors, rather than the magnitude of their values. Different normalization functions are used based on the type of data and the normalization goal.