Improve your machine learning model with better training data
Built for classification and regression models on structured data, such as fraud detection, load forecasting and disease prediction.
Request demoTrusted for AI model training by leading organisations
Where real training data holds your model back
The cases that matter are the rarest
Fraudulent transactions and sick patients are what the model has to predict, and they are the examples you have fewest of. A heavily imbalanced dataset teaches the model to overlook them.
Too little usable data
Missing values, too few records and too little variation limit what a model can learn. You may know a situation can occur and still have no measurements of it. Privacy rules can put the data out of reach altogether.
Bias in the data ends up in the model
If one gender or ethnicity dominates the training data, the model's predictions inherit that skew. For high-risk AI systems, that is also a compliance problem.
What the training data has to deliver, and how BlueGen does it
Relationships between variables that hold up
A model makes its decisions on how several variables move together. Those higher-order relations have to behave the same way in the synthetic data as in the real data.
Relations learned from the real data, then tested
BlueGen's model learns the coherence between variables rather than each column on its own. The evaluation report checks the result with downstream machine learning utility tests and a feature importance comparison between real and synthetic data.
Control over what your model struggles with
Every dataset and every training task has its own weak spot. You decide what the synthetic data should fix: balance, rare examples or gaps.
Balancing, rare-class generation and imputation
Rebalance a column, for example from 80% men and 20% women to 50/50, and the variables that depend on gender shift along with it. Generate more, and more varied, examples of the rare class you care about. Fill missing values in a way that reflects the rest of the data.
Privacy and AI Act readiness
Training data must not expose individuals. If your model is a high-risk AI system under the AI Act, it also has to meet requirements on accuracy, security and fairness.
Balanced data with privacy evaluated
More balanced data, with more examples of underrepresented groups, supports those accuracy, security and fairness requirements. The evaluation report covers privacy next to quality, so both are documented before the data goes into training.
More than 10% higher prediction accuracy
Schneider Electric added BlueGen synthetic data to the training set of its algorithm, and prediction accuracy rose by more than 10%. In practice, the algorithm got clearly more of its predictions right once synthetic data was part of what it learned from.
See what synthetic data can do for your model
- Bring a training task you are working on
- See how BlueGen would balance and extend it