Understanding the Logistic Regression Classifier

The Logistic Regression Classifier (LRC) is a machine-learning algorithm that can automatically create a business rule or a Verity Topic from a set of positive and negative (optional) exemplary documents. Positive documents refer to documents that are relevant to the category of interest. Negative documents are documents that are irrelevant to the category of interest.

LRC automatically learns a classification rule in terms of a Verity topic such that the positive exemplary documents can be maximally distinguished from the negative exemplary documents using this rule in the presence of negative exemplary documents. During the training process, LRC automatically identifies important positive and negative evidence terms from the exemplary documents and computes a numerical weight for each evidence term. The weight value is positive for positive evidence terms and negative for negative evidence terms. The absolute value of a weight indicates the importance of its corresponding evidence term to the topic or category. The larger this absolute value is, the more important the evidence term is to the topic or category.