AI Studio Step 4: Validation#

Profiles: Model Creator, Data Scientist

As a model creator or data scientist, the fourth step in AI Studio is validation.

This page explains the validation functionality within AI Studio.

Overview#

In the Validation section you can see the performance of your model tested on the ground truth.

The model gets tested only on unseen data, due to the k-fold validation approach.

Usage#

All models within a project are listed in the Validation Dashboard as shown below:

Note: The Accuracy of the model is shown in the last column after the training process is finished.

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Tip: To see the detailed validation of a model, click Validate.

The performance of a model is shown in the Validate Model screen. In addition to common performance metrics and the confusion matrix, there is a detailed view of text extracts that have been classified correctly or incorrectly, as shown below:

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In case the performance of the trained model stays below expectations, it can be helpful to try one of the following tips:

  • Try another template.

  • Check which examples got miss classified and add more similar example.

  • Add more examples to the ground truth.

  • Try to build a successful proximity rule model first and use the bulk labeling feature to expand your ground truth set.