9. A machine learning system that quantifies relevancy of a document to a training corpus, the system comprising: at least one server comprising a processor configured to execute instructions that reside in memory, the instructions comprising: a classifier module that: calculates an internal best match score for a relevant document relative to each training example in the training corpus by: determining cosine distances between the document and training examples in the training corpus relative to term frequency-inverse document frequency weights associated with the training examples; and determines the training example having a closest cosine distance to the relevant document; and a user interface module that outputs the training example having the closest cosine distance to the relevant document; wherein the classifier module determines the training example having the closest cosine distance to the relevant document by ranking the training examples by stretching the internal best match scores for the training examples linearly to cover a complete unit interval.
9. A machine learning system that quantifies relevancy of a document to a training corpus, the system comprising: at least one server comprising a processor configured to execute instructions that reside in memory, the instructions comprising: a classifier module that: calculates an internal best match score for a relevant document relative to each training example in the training corpus by: determining cosine distances between the document and training examples in the training corpus relative to term frequency-inverse document frequency weights associated with the training examples; and determines the training example having a closest cosine distance to the relevant document; and a user interface module that outputs the training example having the closest cosine distance to the relevant document; wherein the classifier module determines the training example having the closest cosine distance to the relevant document by ranking the training examples by stretching the internal best match scores for the training examples linearly to cover a complete unit interval. 10. The machine learning system according to claim 9 , wherein each of the training examples has been converted into a high-dimensional feature space using term frequencies.