Benchmark Report
Check out this Benchmark report, running Gretel models on popular ML datasets, indexed by industry
Last updated
Check out this Benchmark report, running Gretel models on popular ML datasets, indexed by industry
Last updated
You can use a Benchmark report like the one shown here to evaluate which Gretel model is best for your synthetic data goals.
For example, Gretel Navigator Fine Tuning consistently generates synthetic data with high Synthetic Data Quality Score (SQS) on multiple types of tabular data, and Gretel ACTGAN is great for particularly long or wide datasets.
Depending on your specific goals with synthetic data or constraints, you may find particular Gretel models to be best suited for your use case. You can reference the Benchmark report below to guide how you evaluate Gretel models, or of course, try Benchmark on your datasets.
The publicly available datasets used in this results leaderboard were sourced from the following ML dataset repositories: UCI, Kaggle, and HuggingFace.
Input Data | Model | SQS | Data Privacy Score | Time (sec) | Size | Datatype | Cols | Rows |
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Input data | Model | SQS | Data Privacy Score | Time (sec) | Size | Datatype | Cols | Rows |
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Input data | Model | SQS | Data Privacy Score | Time (sec) | Size | Datatype | Cols | Rows |
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Input data | Model | SQS | Data Privacy Score | Time (sec) | Size | Datatype | Cols | Rows |
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Input data | Model | SQS | Data Privacy Score | Time (sec) | Size | Datatype | Cols | Rows |
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Input data | Model | SQS | Data Privacy Score | Time (sec) | Size | Datatype | Cols | Rows |
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Input data | Model | SQS | Data Privacy Score | Time (sec) | Size | Datatype | Cols | Rows |
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Input data | Model | SQS | Data Privacy Score | Time (sec) | Size | DataType | Columns | Rows |
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