2023:Confidence Multiplier and Output Confidence (Property)

This article is about an older version of Grooper.

Information may be out of date and UI elements may have changed.

2025

2023

Some results carry more weight than others. The Confidence Multiplier and Output Confidence properties allow you to manually adjust an extraction result's confidence.

You may download the ZIP(s) below and upload it into your own Grooper environment (version 2023). The first contains a Project with resources used in examples throughout this article. The second contains one or more Batches of sample documents.

About

The Confidence Multiplier and Output Confidence properties of Data Type and Data Format extractors allow you to manually alter the confidence score of returned values.

Use of these properties is sometimes referred to as weighted rules. Its practical application allows a user to increase or decrease the confidence score of an extractor's result (or set its confidence to an assigned value). This changes the confidence of the extractor's results, making them appear more (or less) favorable. When used in combination with the Order By property set to Confidence on a parent Data Type, you can manipulate which child extractor's result the parent prioritizes.

General Usage - Confidence Multiplier

Modifying the Confidence Multiplier property of a Data Type or Data Format is done by clicking on the ellipses in the Result Options property which opens the Result Options submenu.

The Confidence Multiplier property defaults to 1 and can be changed in this submenu. The field is a double and takes floating point values.

For example, a value of 0.5 will multiply the confidence of output results by 0.5. If the output confidence was 100%, now it will be 50%. Similarly, you can increase the confidence, even above 100%. If the Confidence Multiplier property is set to 3, and an output result had a 50% confidence, it will not display as 150% confidence.

General Usage - Output Confidence

Modifying the Output Confidence property of a Data Type or Data Format is also done by clicking on the ellipses in the Result Options property which opens the Result Options submenu.

The Output Confidence property defaults to 0% and can be changed in this submenu. The default of 0% will not alter the results confidence scores. Changing this number will override whatever the result's original confidence is and replace it with this value.

For example, a value of 75% will change the confidence of output results to 75%. If the output confidence was 100%, now it will be 75%. If the output confidence was 50%, now it will be 75%. If it was 75%, it will now be (you guessed it) 75%. It doesn't matter what the original confidence was, it will be transformed to the Output Confidence value.

Waterfall Classification

Setting the Classification Method property on a Content Model to Lexical or Rules-Based, one can set up Positive Extractors on Document Types. If this extractor returns a result above the Minimum Similarity set on the Content Model, the document will be assigned that Document Type during classification. By default a result from an extractor is returned at 100% confidence (unless Fuzzy RegEx is leveraged to return a result, in which case the confidence will be affected by the fuzzy algorithm.) Given this fact positive extractors are almost certain to be above the Minimum Similarity.

This extractor could be a "Waterfall Extractor", taking advantage of the Waterfall Extraction technique. However, for classification, the system is just looking for some result to be returned above the Minimum Similarity confidence threshold.

In the Waterfall Classification method, the Minimum Confidence property can be set in the Result Filter property window of a Data Type which will eliminate any results less than that confidence. This may eliminate the results of some referenced extractors which technically matched, but at a low percent.

If we happen to know that those lower confidence hits are valid and should count for classifying the document, then the Confidence Multipliers on those referenced Data Types can be set to a higher value in order to make them hit the Minimum Confidence required.

Similarly, if higher confidence hits are inappropriately classifying documents and shouldn't be returned, the Confidence Multiplier property can be reduced so that those Data Types only exceed the Minimum Confidence when they are very high confidence.

Example

In the example below, we are going to use the Project and Batch that accompany the Document Classification 2023 course on Grooper University.

Right now we're looking at how documents are currently being classified by working in a Classify Batch Process Step. We see that this Title Opinion is being misclassified as a Generic Letter. Notice that the document has a similarity score of 100% for the Generic Letter Document Type and a 68% score for the Title Opinion Document Type.
If we go to the Content Model... We can see that our Minimum Similarity property is set to 55%. Both the Generic Letter and the Title Opinion Document Types came in at above the Minimim Similarity percentage, but the Generic Letter won out at a higher percentage.
Let's look at the Generic Letter Document Type. The Positive Extractor is set to a reference.
Let's look at the extractor that is being referenced. We're going to scroll down to the "OUTPUT" section in the Data type "Properties" tab, and click the ellipsis button next to Result Options.
When the "Result Options" window pops up, we see that by default the Confidence Override is set to 0%. If we set this property to anything other than 0%, when a document is classified, whatever Document Type is using this extractor will have a similarity score no higher than that number.
We're going to set the Confidence Override to 60%. Click "OK" to apply the new settings.
With our settings updated, let's go back to the Classify Batch Process Step. On the "Classification Tester" tab we have reclassified the documents. Notice that the Title Opinion document is now being classified appropriately. The Title Opinion Document Type is still coming in at 68%. However, the Generic Letter Document Type is returning with a 60% similarity score due to the Confidence Override property we set.
This Generic Letter is still being classified as a Generic Letter Document Type. We see that although the Generic Document Type has a 60% similarity score, it is still higher than the Minimum Similiarity score of 55% and it is also higher than any other Document Type

Glossary

Batch Process Step: edit_document Batch Process Steps are specific actions within a settings Batch Process sequence. Each Batch Process Step performs an "Activity" specific to some document processing task. These Activities will either be a "Code Activity" or "Review" activities. Code Activities are automated by Activity Processing services. Review activities are executed by human operators in the Grooper user interface.

Batch Process Steps are frequently referred to as simply "steps".
Because a single Batch Process Step executes a single Activity configuration, they are often referred to by their referenced Activity as well. For example, a "Recognize step".

Batch: inventory_2 Batch nodes are fundamental in Grooper's architecture. They are containers of documents that are moved through workflow mechanisms called settings Batch Processes. Documents and their pages are represented in Batches by a hierarchy of folder Batch Folders and contract Batch Pages.

Classification Method:

Classification: Classification is the process of identifying and organizing documents into categorical types based on their content or layout. Classification is key for efficient document management and data extraction workflows. Grooper has different methods for classifying documents. These include methods that use machine learning and text pattern recognition. In a Grooper Batch Process, the Classify Activity will assign a Content Type to a folder Batch Folder.

Classify: unknown_document Classify is an Activity that "classifies" folder Batch Folders in a inventory_2 Batch by assigning them a description Document Type.

Classification is key to Grooper's document processing. It affects how data is extracted from a document (during the Extract activity) and how Behaviors are applied.
Classification logic is controlled by a Content Model's "Classify Method". These methods include using text patterns, previously trained document examples, and Label Sets to identify documents.

Confidence Multiplier and Output Confidence: Some results carry more weight than others. The Confidence Multiplier and Output Confidence properties allow you to manually adjust an extraction result's confidence.

Content Model: stacks Content Model nodes define a classification taxonomy for document sets in Grooper. This taxonomy is defined by the collections_bookmark Content Categories and description Document Types they contain. Content Models serve as the root of a Content Type hierarchy, which defines Data Element inheritance and Behavior inheritance. Content Models are crucial for organizing documents for data extraction and more.

Data Type: pin Data Types are nodes used to extract text data from a document. Data Types have more capabilities than quick_reference_all Value Readers. Data Types can collect results from multiple extractor sources, including a locally defined extractor, child extractor nodes, and referenced extractor nodes. Data Types can also collate results using Collation Providers to combine, sift and manipulate results further.

Document Type: description Document Type nodes represent a distinct type of document, such as an invoice or a contract. Document Types are created as child nodes of a stacks Content Model or a collections_bookmark Content Category. They serve three primary purposes:

They are used to classify documents. Documents are considered "classified" when the folder Batch Folder is assigned a Content Type (most typically, a Document Type).
The Document Type's data_table Data Model defines the Data Elements extracted by the Extract activity (including any Data Elements inherited from parent Content Types).
The Document Type defines all "Behaviors" that apply (whether from the Document Type's Behavior settings or those inherited from a parent Content Type).

Extract: export_notes Extract is an Activity that retrieves information from folder Batch Folder documents, as defined by Data Elements in a data_table Data Model. This is how Grooper locates unstructured data on your documents and collects it in a structured, usable format.

Lexical: "Lexical" is a Classify Method that classifies folder Batch Folders based on the text content of trained document examples. This is achieved through the statistical analysis of word frequencies that identify description Document Types.

Project: package_2 Projects are the primary containers for configuration nodes within Grooper. The Project is where various processing objects such as stacks Content Models, settings Batch Processes, profile objects are stored. This makes resources easier to manage, easier to save, and simplifies how node references are made in a Grooper Repository.

Rules-Based: "Rules-Based" is a Classify Method that employs "rules" defined on each description Document Type to classify folder Batch Folders. Positive Extractor and Negative Extractor properties are configured for each Document Type to positively or negatively associate a Batch Folder based on predefined criteria.

Where the Positive and Negative Extractors will impact all Classify Method results, the Rules-Based method classifies using only these properties and nothing else.

Waterfall Classification: Waterfall Classification is a classification technique in Grooper that prioritizes training similarity over classification "rules" set by a description Document Type's Positive Extractor. This can be helpful in scenarios where folder Batch Folders get misclassified and simply retraining won't help.