❖ Targeted Sampling
The Targeted Sampling bundle allows you to create synthetic data that is specific and targeted to your usage. You can use targeted sampling to:
⭐ Create minimum viable synthetic test sets for software testing and QA. Define the exact values that you need across multiple tables and let SDV handle the rest.
⭐ De-bias and rebalance your datasets. Adjust the proportions of values to generate in the synthetic data to create a more balanced, fairer representation of the data you'd like to use for ML development.
⭐ Extrapolate hypothetical scenarios. Create edge cases and situations that don't exist in the real, but are theoretically possible to observe. Using this bundle, you can extrapolate outside of the boundaries of your real training data to create a wider variety of synthetic data.
Included Features
Key features of the Targeted Sampling bundle are:
Specifying ranges for extrapolation. Before training a synthesizer, you can specify the full range of values you'd like to see in the synthetic data — even if those ranges are outside the boundaries of your training data. This allows you to extrapolate and create new, hypothetical scenarios.
Conditional sampling. After training your synthesizer, you can provide exactly the conditions you wish to create. The rest of the synthetic data will adapt to those conditions..
Installation
Use your SDV Enterprise credentials to install SDV Enterprise and all bundles that you have access to.
% pip install sdv-installer --upgrade
% sdv-installer install --upgradeUsername: <email>
License Key: ********************************
Installing SDV Enterprise:
sdv-enterprise (version 0.30.0) - Installed!
Installing Bundles:
bundle-cag - Installed!
bundle-xsynthesizers - Installed!
Success! All packages have been installed. You are ready to use SDV Enterprise.Last updated