Document samples are trained as examples of a Document Type. Training occurs via user supervised machine learning using the TF-IDF algorithm. A Data Extractor set on the Text Feature Extractor property returns words, phrases or other results from the extractor to provide possible identifiers used to classify a document. These identifiers (the words, phrases or other results from the Data Extractor used) are called "features." Document training uses TF-IDF to assign weightings to those features. During classification, Grooper looks at the weightings list for the various trained Document Types and compares them to the features extracted by the Text Feature Extractor on the current document to be classified. The document is then assigned a percentage similarity score to each possible Document Type match. Whichever Document Type has the highest percentage similarity is used to classify the document.