Which of the following is a best practice when choosing a UiPath ML (Machine Learning) Extractor?
A.
The popularity of the ML Extractor among other UiPath users should be the primary factor when choosing a UiPath ML Extractor.Opt for the ML Extractor that has the highest number of downloads or positive reviews.
B.
Consider the document types, language, and data quality when choosing an ML Extractor.It is important to select one that is specifically trained or optimized for the document types being processed.It is also important to take into account the quality and diversity of the training data used to train the ML Extractor to ensure accurate and reliable extraction results.
C.
The cost of the ML Extractor should be the main consideration when choosing an ML Extractor.Select the ML Extractor that offers the lowest price, regardless of its performance or suitability for the specific document understanding needs.
D.
The size of the ML Extractor is the most important factor to consider when choosing an ML Extractor.Bigger models always perform better and provide more accurate extraction results because the development team invested time and effort into creating the algorithm, which in turn will result in better performance for the trained model.
The ML Extractor is a data extraction tool that uses machine learning models provided by UiPath to identify and extract data from documents. The ML Extractor can work with predefined document types, such as invoices, receipts, purchase orders, and utility bills, or with custom document types that are trained using the Data Manager and the Machine Learning Classifier Trainer12.
According to the best practice, the ML Extractor should be chosen based on the document types, language, and data quality of the documents being processed. It is important to select an ML Extractor that is specifically trained or optimized for the document types that are relevant for the use case, as different document types may have different layouts, fields, and formats. It is also important to take into account the language of the documents, as some ML Extractors may support only certain languages or require specific language settings. Moreover, it is important to consider the quality and diversity of the training data used to train the ML Extractor, as this may affect the accuracy and reliability of the extraction results. The training data should be representative of the real-world data, and should cover various scenarios, variations, and exceptions3.
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