Transcription of CHAPTER Logistic Regression - Stanford University
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CHAPTER Logistic Regression - Stanford University
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyrightc 2019. Allrights reserved. Draft of October 2, Regression And how do you know that these fine begonias are not of equal importance? Hercule Poirot, in Agatha Christie sThe Mysterious Affair at StylesDetective stories are as littered with clues as texts are with words. Yet for thepoor reader it can be challenging to know how to weigh the author s clues in orderto make the crucial classification task: deciding this CHAPTER we introduce an algorithm that is admirably suited for discoveringthe link between features or cues and some particular outcome: Logistic , Logistic Regression is one of the most important analytic tools in the socialand natural sciences. In natural language processing, Logistic Regression is the base-line supervised machine learning algorithm for classification, and also has a veryclose relationship with neural networks.
dot product In the rest of the book we’ll represent such sums using the dot product notation from linear algebra. The dot product of two vectors a and b, written as ab is the sum of the products of the corresponding elements of each vector. (Notice that we represent vectors using the boldface notation b). Thus the following is an equivalent
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