Amazon SageMaker Canvas supports mathematical functions and operators for richer data exploration
Amazon SageMaker Canvas now supports mathematical functions and operators for richer data exploration, allowing you to define new features in your data. SageMaker Canvas is a visual point-and-click service that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code.
Amazon SageMaker Canvas now supports mathematical functions and operators for richer data exploration, allowing you to define new features in your data. SageMaker Canvas is a visual point-and-click service that enables business analysts to generate accurate ML predictions on their own — without requiring any machine learning experience or having to write a single line of code.
SageMaker Canvas supports a number of data transformations to filter, join, and modify datasets, and advanced visualizations to understand the relationships between variables in your data. Starting today, you can use mathematical functions and logical operators with these data transformations to understand the distribution of your data better prior to building ML models. The results from these functions and operators allow you to create new features that can be visualized for particular attributes. Supported mathematical functions include add, subtract, multiply, divide, mean, standard deviation, variation, exponent, and log. Additionally, SageMaker Canvas supports logical operators such as if-then-else statements to define specific conditions and gives you the flexibility to understand, distribute, and explore your data better.
With this new capability, SageMaker Canvas enables binning - a data pre-processing technique. Binning is a method to group related numerical or categorical values into a smaller number of sets called bins. As an example, if you have a dataset tracking furniture items, you can group them in different bins such as office furniture, living room furniture, or bedroom furniture. Binning helps you identify outliers, invalid values, and reduces non-linearity in datasets, thereby improving the accuracy of your ML models.
The ability to define new features using mathematical functions and operators, and use these features to distribute data in separate logical bins is now available in all AWS regions where SageMaker Canvas is supported. To learn more about SageMaker Canvas and get started, please see the product page and technical documentation.