Dataset distribution analysis for Machine Learning validation
Service Description
Overview
This service provides a statistical analysis of datasets and dataset splits used in machine learning pipelines, with the objective of verifying their distributional consistency. It supports the validation of training, validation, and test splits by detecting statistically significant differences that could bias model training or invalidate performance evaluation. The analysis is conducted in a controlled and documented execution environment to ensure traceability and repeatability of results.
How can the service help you?
The service is intended for SMEs and technology developers that need to determine whether the datasets used to develop and validate an AI model are sufficiently consistent and appropriate for reliable performance evaluation. The service is particularly relevant for AI systems in the health domain, where dataset representativeness directly affects the evaluation reliability, which is critical for regulatory compliance and clinical trustworthiness.
Before the service, the customer may have defined training, validation, and test datasets but may not know whether differences between their distributions could introduce bias, affect model performance, or make validation results difficult to interpret. In healthcare applications, this can be particularly important when datasets contain different patient populations, acquisition conditions, sites, devices, or other application-specific characteristics.
Multitel brings expertise in AI testing, dataset analysis, and statistical evaluation to characterize and compare the relevant data distributions and identify differences that should be considered when interpreting model performance.
After the service, the customer receives a documented analysis of the dataset distributions, highlighting relevant similarities, differences, or potential distribution shifts and their implications for machine learning validation. The results can support decisions on dataset splitting, additional data needs, and the design or interpretation of subsequent model performance evaluation.
How will the service be delivered?
The service starts with the preparation and discussion of a Welcome Package, used to align Multitel and the customer on the objectives and expected outcomes of the analysis before its execution. It covers the intended application, available datasets and dataset splits, relevant variables or subgroups, the comparisons to be performed, the practical conditions for executing the tests and the expected reporting of results. When needed, Multitel supports the customer in defining the most relevant analyses according to the intended machine learning validation objective.
Once the Welcome Package has been agreed, Multitel performs the analysis in a controlled and documented execution environment. The analysis is performed using robust statistical techniques, adapted to the type of data and the agreed objectives, to characterize and compare the relevant dataset distributions. All activities are performed under ISO 9001:2015 certified processes.
At the end of the service, the results are delivered as a structured and interpretable report describing the analyses performed, the identified distributional differences, and their potential implications for machine learning development and validation. The results are discussed with the customer to support their interpretation.
The analysis is performed by Multitel’ s experts and does not require the customer to be on site. Customer interactions can be organized remotely. Medical or clinical expertise is not systematically required but may be needed to determine the clinical relevance of particular variables, subgroups, or observed differences. The execution time depends on the number, size, and complexity of the datasets and on the analyses agreed in the Welcome Package and is defined during the quotation phase.
Use cases/examples
Example – Distribution analysis before AI model performance validation
A healthcare technology developer has prepared training, validation, and independent test datasets for an AI-based medical application and wants to ensure that differences between these datasets will not compromise the interpretation of the model performance evaluation.
Multitel characterizes the datasets and compares relevant variables and subgroups across the different splits. The analysis identifies statistically significant distributional differences and assesses whether these differences should be considered when interpreting the subsequent model performance results.
Outcome: a documented dataset distribution analysis identifying relevant differences between the development and evaluation datasets, their potential impact on model validation, and recommendations for additional analyses or dataset adjustments where needed.
Provider & Contact
Operational Details
- AI Act
- MDR
- AI Act Art. 10
- MDR