Customer Ratings, Letter Grades, and Other Rankings: Using Deep Learning When Class Labels Have A Natural Order
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.
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. His recent research projects have focused on general challenges such as few-shot learning for working with limited data and developing deep neural networks for ordinal targets. As Lead AI Educator at Grid.ai, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.