Machine learning training models can sometimes be very noisy and highly inaccurate. Imprecise Ml models can lead to a messy outcome, and data scientists have to deal with hundreds of such ML models over a course of time before picking the best machine learning training that comes with superior labeling. However, not all ML models can be labeled, and that brings us to discuss the current trends related to “Weak Supervision” in machine learning.
What is Weak Supervision Machine Learning?
Weak supervision machine learning is defined as a sub-field in AI training that allows for the injection of subject level inputs and information to label machine data. The weak machine learning approach is taken by data scientists when other models for labeling data don’t suffice. For example, weak supervision can be used in place of traditional supervision, semi-supervised learning, or transfer learning (also referred to as pre-trained modeling).
In the modern era of automated machine learning techniques, weak supervision proves to be a very handy tool by virtue of its easy resourcing, quick results, and above all, controlled injection of domain knowledge that delivers both interpretations as well as control on any top level AI algorithm used for domain specific purposes, such as customer chat support, voice marketing, or AI-based data mining solutions.
Types of Weak Supervision Labels
When we speak of weak supervision in machine learning training courses, we have to deal with different types of labeling methods. These are discussed below:
Inexact labeling; Also referred to as an imprecise weak supervision technique as it deals with the SME guidelines associated with mathematical optimization rules such as heuristic rules.
Inaccurate labeling; is a straight-forward assumption used to crowdsource data / raw feed that are presumed to be inaccurate, but useful nonetheless in building a machine learning model (for example, population of illiterate women / men in a given region where there are less than 50 schools); or output per hectare of wheat crop in drought hit region of Maharashtra, etc.
Existing labels: These associate with the available labels extracted and injected from knowledge bases, graphs, and pre-trained ML models that can be speculated to be delivering a reasonably weak, but useful machine learning training dataset.
Popular works in the field of Weak Supervision Machine Learning training
In my years of experience as a data scientist working with top ML execution projects, on weak supervision training project that made the biggest impact on my learning has to be “Snorkel”.
Snorkel was presented by Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré, who defined this cutting edge technique as a simple solution to problems associated with the labeling of machine learning datasets, providing users with an optimized system to train any kind of ML model without actually manual labeling of data. It is carried forward using hand coded arbitrary heuristics, and despite accuracies and imperfections, can still be implemented within any modern machine learning paradigm.
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