Ratings and Rankings -- Using Deep Learning When Class Labels Have A Natural Order
Sebastian's books: https://sebastianraschka.com/books/
Deep learning offers state-of-the-art results for classifying images and text. Common deep learning architectures and training procedures focus on predicting unordered categories, such as recognizing a positive and negative sentiment from written text or indicating whether images contain cats, dogs, or airplanes. However, in many real-world problems, we deal with prediction problems where the target variable has an intrinsic ordering. For example, think of customer ratings (e.g., 1 to 5 stars) or medical diagnoses (e.g., disease severity labels such as none, mild, moderate, and severe). This talk will describe the core concepts behind working with ordered class labels, so-called ordinal data. We will cover hands-on PyTorch examples showing how to take existing deep learning architectures for classification and outfit them with loss functions better suited for ordinal data while only making minimal changes to the core architecture.
Slides: https://sebastianraschka.com/pdf/slid...
Code: https://raschka-research-group.github...
0:00 Introduction
0:32 Many Real-World Predictions Problems Have Ordered Labels
0:57 Ordered Labels? Tell Me More!
3:59 Can't we just use regular classifiers for ordered labels?
5:47 How? Let's (Re)Use What We Already know: An Extended Binary Classification Framework
8:07 Problem: rank inconsistency
10:53 Converting a Classifier into a CORN Model in 3 Lines of Code
13:09 Acknowledgements
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