UiPath UiPath-SAIAv1 - UiPath Specialized AI Associate Exam (2023.10)

UiPath offers several pre-trained out-of-the-box models, and Invoices is one of the most commonly supported document types. This model is optimized to extract relevant fields such as invoicenumbers, dates, amounts, and more from structured invoices. Other common out-of-the-box models include receipts and purchase orders.

The Mean Average Precision (MAP) is the primary metric used in UiPath Communications Mining to assess the performance of classification models, specifically for the "All Labels" factor. MAP evaluates how well the model predicts labels by considering both the precision and recall of the predictions. This metric is commonly used in information retrieval systems to provide an overall measure of accuracy across all labels in the dataset.

(Source: UiPath Communications Mining documentation)

his is confirmed in the UiPath documentation, where the "Train" feature guides users from the dataset creation stage through the necessary steps to optimize the training process. The system recommends next best actions based on progress to ensure efficient and focused training​

In UiPath Communications Mining, the term Coverage refers to the proportion of messages in the dataset that have informative label predictions. This is an important metric that indicates how much of the dataset the model is able to classify with meaningful and relevant labels. High coverage means that the model is effectively assigning labels to a large portion of the data, which is crucial for ensuring the model’s usefulness in automating communication mining tasks.

For more details, refer to:

UiPath Communications Mining Performance Factors: Model Coverage and Accuracy

Communications Mining: Coverage and Other Metrics