Tabular Data
This page provides examples of how RagaAI's Testing Platform can add value to teams building tabular data models. It is a companion piece to the Product Demo available on the RagaAI Platform.
The Tabular Data Project on the sample workspace is an example of how the RagaAI Testing Platform can help with the following tasks -
Data Quality Checks before training a new model
Model Quality Checks to identify performance gaps and perform regression analysis
End-to-end pipeline level tests beyond AI models
The RagaAI Testing Platform is designed to add science to the art of detection AI issues, performing root cause analysis and providing actionable recommendations. This is done as an automated suite of tests on the platform.
An overview of all tests for the sample project is available here -
1. Failure Mode Analysis
Goal - Identify specific image scenarios where the Default Prediction model underperforms, despite overall acceptable performance metrics.
Methodology - RagaAI automatically detections scenarios within the dataset and brings any model vulnerabilities on such scenarios to the fore
Insight - In this case, we see that the model struggles with specific datapoints in the dataset. Analysing the images within the failing cluster can reveal common characteristics that lead to misclassification. This helps understand the model's limitations and potential biases.
Impact - Early identification of failure modes allows for targeted interventions, such as collecting more data for the under-represented cluster to improve model training or fine-tuning the model with specific emphasis on the failing cluster.
For more details, please refer to the detailed Failure Mode Analysis documentation.
Last updated