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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Multiclass Logistic Regression
The extension to Logistic Regression, for classifying more than two classes, is Multiclass Logistic Regression. Its foundation is actually a generic approach: it doesn't just work for Logistic Regressors, it also works with other binary classifiers. The base algorithm is named One-vs-rest, or One-vs-all, and it's simple to grasp and apply.
Let's describe it with an example: we have to classify three kinds of flowers and, given some features, the possible outputs are three classes: f1, f2, and f3. That's not what we've seen so far; in fact, this is not a binary classification problem. Instead, it seems very easy to break down this problem into three simpler problems:
- Problem #1: Positive examples (that is, the ones ...
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