Prediction
Leveraging Prediction Functionality in Dataverse
Last updated
Leveraging Prediction Functionality in Dataverse
Last updated
Dataverse offers a powerful prediction feature that allows users to test the performance of a selected AI model on a target data slice. This functionality enables quick visualization of the model's predictions, making it easy for users to evaluate the model's performance.
Additionally, if the target data slice includes ground truth labels, the platform will automatically calculate performance metrics, allowing users to compare the performance of different models on the same dataset.
Choose the AI model you wish to use for making predictions on your target data slice.
Initiate the prediction process by clicking the "Prediction" button. Dataverse will process the input data using the selected AI model and generate the predicted output.
Dataverse will display the prediction results in a user-friendly visual format, allowing you to quickly assess the model's performance. Observe patterns and trends in the predictions to identify potential areas for improvement.
If ground truth labels are available for the target data slice, Dataverse will automatically calculate performance metrics, such as accuracy, precision, recall, and F1 score. These metrics enable you to evaluate and compare the performance of different models on the same dataset.
By leveraging the prediction functionality in Dataverse, you can gain valuable insights into your AI model's performance and make data-driven decisions for model optimization and deployment. This feature streamlines the evaluation process and facilitates model comparison, ensuring that you select the best model for your specific use case.
