February 2016
Beginner to intermediate
312 pages
7h 23m
English
Content preview from Regression Analysis with PythonBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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SGD classification with hinge loss
In Chapter 4, Logistic Regression we explored a classifier based on a regressor, logistic regression. Its goal was to fit the best probabilistic function associated with the probability of one point to be classified with a label. Now, the core function of the algorithm considers all the training points of the dataset: what if it's only built on the boundary ones? That's exactly the case with the linear Support Vector Machine (SVM) classifier, where a linear decision plane is drawn by only considering the points close to the separation boundary itself.
Beyond working on the support vectors (the closest points to the boundary), SVM uses a new decision loss, called hinge. Here's its formulation:
Where t is the intended ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.