Data Transformation - Scale and Reduce

This article describes the modules in Azure Machine Learning Studio that can help you work with numerical data. For machine learning, common data tasks include clipping, binning, and normalizing numerical values. Other modules support dimensionality reduction.

Note

Applies to: Machine Learning Studio

This content pertains only to Studio. Similar drag and drop modules have been added to the visual interface in Machine Learning service. Learn more in this article comparing the two versions.

Modeling numerical data

Tasks such as normalizing, binning, or redistributing numerical variables are an important part of data preparation for machine learning. The modules in this group support the following data preparation tasks:

  • Grouping data into bins of varying sizes or distributions.
  • Removing outliers or changing their values.
  • Normalizing a set of numeric values into a specific range.
  • Creating a compact set of feature columns from a high-dimension dataset.

List of modules

This Data Transformation - Scale and Reduce category includes the following modules:

See also