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Introduction to Positive Unlabeled (PU) Learning | DataHour | Analytics Vidhya
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Positive-Unlabeled (PU) learning is a Machine Learning approach to Binary Classification where the training data comprises of positive instances as well as an additional unlabeled data that might contain positive and negative instances in unknown proportions. Positive-unlabeled learning methods aim to incorporate this unique scenario into the learning process, in a way that improves generalization of the learned representations of the positive class, when compared to simply treating all unlabeled instances as purely negative instances, or alternatively discarding them and training a one-class classifier over only the positive samples.
In this DataHour, Chandra will explain all about Positive Unlabeled learning including its basics, use and practical applications.
Sections
00:00:00 Introduction
00:02:20 Machine Learning: Quick Brush-up
00:04:48 What is Positive Unlabeled Learning
00:06:69 Different Approaches to PU Learning
00:10:52 Hands On PU Learning
00:39:44 Some Real-World Application
00:47:46 Brief Q&A Session
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In this DataHour, Chandra will explain all about Positive Unlabeled learning including its basics, use and practical applications.
Sections
00:00:00 Introduction
00:02:20 Machine Learning: Quick Brush-up
00:04:48 What is Positive Unlabeled Learning
00:06:69 Different Approaches to PU Learning
00:10:52 Hands On PU Learning
00:39:44 Some Real-World Application
00:47:46 Brief Q&A Session
Stay on top of your industry by interacting with us on our social channels:
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