Can any of you smart people explain the difference in how the Intelligent Keyword Classifier learns vs. how the Machine Learning Classifier learns? I think I know that the IK classifier weights keywords based on frequency? I have no idea how the ML classifier learns. Can someone explain it to a newbie? I don’t need a detailed explanation, just a general overview, so that I know the difference in how it works.
The Intelligent Keyword Classifier (IK classifier) and the Machine Learning Classifier (ML classifier) learn and operate in fundamentally different ways. The IK classifier primarily relies on keyword matching, where it assigns weights to keywords based on their frequency and importance within the dataset. It uses these weighted keywords to classify new data by looking for the occurrence and significance of these keywords in the input.
On the other hand, the ML classifier learns from data through a process called training, where it is fed a large dataset with labeled examples. The ML classifier uses algorithms to identify patterns, correlations, and rules within the data. It builds a model that can make predictions or classifications by generalizing from the training data. Unlike the IK classifier, which relies on predefined keywords, the ML classifier can adapt and improve its accuracy over time as it is exposed to more data.
So basic difference is inteligent keyword classifier works more as a levingstein algorithm…where the word vector weight is calculated to understand the distance from the actual words and give the results
On other hand ml classifier users ml models which are trained already on many document types and are based on the backend model which users set of patterns ,keywords,structure etc and on top we can retrain the model for more types
Cheers
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