{"id":68084,"date":"2022-09-02T17:15:13","date_gmt":"2022-09-02T16:15:13","guid":{"rendered":"https:\/\/www.clickworker.com\/?page_id=68084"},"modified":"2022-11-23T09:30:24","modified_gmt":"2022-11-23T08:30:24","slug":"weak-supervision","status":"publish","type":"page","link":"https:\/\/www.clickworker.com\/ai-glossary\/weak-supervision\/","title":{"rendered":"Weak Supervision"},"content":{"rendered":"<section class=\"white section-space top_grey_separation_sm\">\r\n<div class=\"container pb-5\">\r\n<div class=\"row align-items-center justify-content-center justify-content-lg-between\">\r\n<div class=\"col-lg-6 pb-2\">\r\n\r\n<span class=\"gl_letter_offcanvas gl_single\">W<\/span>\r\n<h1 class=\"section-headline pt-4 mt-1 mb-4\"><span class=\"gl_single_headline\">Weak Supervision<\/span><\/h1>\r\n<ul>\r\n \t<li><a href=\"#explanation\">Weak Supervision \u2013 Short Explanation<\/a><\/li>\r\n \t<li><a href=\"#saving\">Saving time with weak supervision<\/a><\/li>\r\n\t<li><a href=\"#benefits\">What are the benefits of weak supervision?<\/a><\/li>\r\n\t\t<li><a href=\"#ml\">How can you use weak supervision for machine learning?<\/a><\/li>\r\n \t<li><a href=\"#frontier\">Weak Supervision presents a new frontier in Machine Learning<\/a><\/li>\r\n\t<li><a href=\"#vs\">Weak Supervision vs. Rule-Based Classifiers<\/a><\/li>\r\n\t\t<li><a href=\"#methods\">What are some common weak supervision methods?<\/a><\/li>\r\n\t\t\t<li><a href=\"#types\">Types of weak supervision<\/a><\/li>\r\n\t<li><a href=\"#labels\">Types of Weak Labels<\/a><\/li>\r\n\t<li><a href=\"#best\">What are some best practices for using weak supervision?<\/a><\/li>\r\n\t<li><a href=\"#implement\">How do you implement weak supervision in your machine learning pipeline?<\/a><\/li>\r\n\t<li><a href=\"#challanges\">What are some common challenges with weak supervision?<\/a><\/li>\r\n\t<li><a href=\"#conclusion\">Conclusion<\/a><\/li>\r\n\t\r\n<\/ul>\r\n<\/div>\r\n\t\r\n<div class=\"col-auto\">\r\n\t<img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/uploads\/2022\/08\/active-learning-ml.png\" alt=\"Weak Supervision\" class=\"gl_image max-width-100\"\/><\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n\r\n<section class=\"gray section-space\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"explanation\">Weak Supervision \u2013 Short Explanation<\/h2>\r\n<p>Do you ever wonder how machine learning algorithms work in the field? If so, it may be because of <strong>weak supervision<\/strong>. Have no fear! In this article, I will try to give a clear definition and explain what is meant by weak supervision as well as provide an example for each type of supervised algorithm.<\/p>\r\n<p>What does that mean? Weak supervision refers to when human oversight or input isn&#8217;t provided into the process concerning which features are useful and important enough for prediction purposes. This often happens when the data set is too big, or the feature space is so large that it&#8217;s impossible for humans to identify all possible features and may result in machines making mistakes.\r\nThe key takeaway from this article: machine learning algorithms work based on a human input which let them know what features are important enough for prediction purposes. In other words, if you want your supervised algorithm to make more accurate predictions without being faulty then you should provide clear instructions as well as input into how much of each particular.\r\n<\/p>\r\n\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>\r\n\r\n<section class=\"white section-space bottom_grey_separation_sm\">\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-lg-12\">\r\n<h2 class=\"section-headline mb-4\" id=\"saving\"><strong>Saving time with weak supervision<\/strong><\/h2>\r\n <p>Have you ever thought why a model projecting results does not work? If you are a user of <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/machine-learning\/\" title=\"machine learning - term explanation\">Machine Learning<\/a>, you know how frustrating is not have &#8220;good&#8221; data to run your models and predict results. In this sense, you can apply weak supervision machine learning as a solution!<\/p>\r\n<p>In a nutshell, weak supervision machine learning provides techniques to categorize unlabeled, unstructured and low-quality data, pour it into a model, and obtain predictive results to meet specific customer-tailored needs. In other words, it is categorizing unlabeled data to be used in your model. These techniques are incredibly useful because there is a lot of available data that are spread out and unstructured (in other words, it is your raw materials in its primitive stage to make a product!)\r\nIt is worth mentioning that Machine Learning needs labeled data to create algorithms, so that these can be used to learn, train a set of activities, or predict customer\u00b4s behaviors. You do not want get inconclusive outcomes by using imprecise data!\r\nWeak supervision machine learning also can be used along with other labeling data techniques (e.g: <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/active-learning\/\" title=\"Acitve learning - term explanation\">Active learning<\/a>, Transfer learning or Semi-supervised learning) which is very handy!<\/p>\r\n<p>Weak supervision machine learning is one of the available solutions that can really save your time to categorize unlabeled data for a learning, training or predicting. It is also a great complementing tool to your existing data structuring techniques and to give you a helping hand to provide reliable outcomes!<\/p>\r\n\r\n<p><strong>&#8211; Weak Supervision Learning Explained by Prolego &#8211;<\/strong><\/p>\r\n\r\n<div class=\"lyte-wrapper\" title=\"Weak Supervision Learning Explained\" style=\"width:420px;max-width:100%;margin:5px;\"><div class=\"lyMe\" id=\"WYL_SS9fvo8mR9Y\" itemprop=\"video\" itemscope itemtype=\"https:\/\/schema.org\/VideoObject\"><div><meta itemprop=\"thumbnailUrl\" content=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FSS9fvo8mR9Y%2Fhqdefault.jpg\" \/><meta itemprop=\"embedURL\" content=\"https:\/\/www.youtube.com\/embed\/SS9fvo8mR9Y\" \/><meta itemprop=\"duration\" content=\"PT25M45S\" \/><meta itemprop=\"uploadDate\" content=\"2021-03-15T18:51:10Z\" \/><\/div><div id=\"lyte_SS9fvo8mR9Y\" data-src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FSS9fvo8mR9Y%2Fhqdefault.jpg\" class=\"pL\"><div class=\"tC\"><div class=\"tT\" itemprop=\"name\">Weak Supervision Learning Explained<\/div><\/div><div class=\"play\"><\/div><div class=\"ctrl\"><div class=\"Lctrl\"><\/div><div class=\"Rctrl\"><\/div><\/div><\/div><noscript><a href=\"https:\/\/youtu.be\/SS9fvo8mR9Y\" rel=\"nofollow\"><img decoding=\"async\" src=\"https:\/\/www.clickworker.com\/wp-content\/plugins\/wp-youtube-lyte\/lyteCache.php?origThumbUrl=https%3A%2F%2Fi.ytimg.com%2Fvi%2FSS9fvo8mR9Y%2F0.jpg\" alt=\"Weak Supervision Learning Explained\" width=\"420\" height=\"216\" \/><br \/>Watch this video on YouTube<\/a><\/noscript><meta itemprop=\"description\" content=\"Getting labeled training data is almost always the hardest part of an NLP program. In this video I describe how to use label functions and weak supervision to programmatically generate labels for text classifiers. The idea is to automatically bootstrap labels based on key words or phrases used by experts familiar with the domain. This simple idea can be formalized into a powerful labeling framework (sometimes called data programming) that probabilistically assigns labels based on these pieces of \u201cvirtual evidence.\u201d We\u2019ve found this approach is especially powerful when paired with recent transformer-based NLP models. I\u2019ll introduce these basic ideas, talk about the open-source library Snorkel that you can start using today, and also discuss some recent research in self-supervision that I\u2019m really excited about. ------------------------------------------------------------------------------------------------------------------------------------------------ \ud83d\udcda READING LIST: Read our book - Become an AI Company in 90 Days: https:\/\/www.prolego.com\/become-an-ai-company Read our research report - AI Natives Among Us: https:\/\/www.prolego.com\/ai-natives Read our comic book - Adventures in AI: shorturl.at\/vHLUY Read our research report - Win the War for Data Science Talent: https:\/\/www.prolego.com\/win-the-war-for-ai-talent \ud83d\udcf9 PROLEGO - GREATEST HITS: Prolego + MIT AI Bootcamp: https:\/\/youtu.be\/fUiezSdnYZ0 High Level Overview of GPT-3: https:\/\/youtu.be\/Rx-5AGHNu7M GPT-2 Demo Produces the Next Games of Thrones book, &#039;The Winds of Winter&#039;: https:\/\/youtu.be\/uoB1R3tsXtE Weak Supervision Learning Explained: https:\/\/youtu.be\/SS9fvo8mR9Y Novelty with Transformers and Clustering: https:\/\/youtu.be\/fV-GLpWtog0 Follow Prolego LinkedIn: https:\/\/www.linkedin.com\/company\/prolego\"><\/div><\/div><div class=\"lL\" style=\"max-width:100%;width:420px;margin:5px;\"><\/div>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=benefits><strong>What are the benefits of weak supervision?<\/strong><\/h2>\r\n<p>Progression in machine learning has increased with models used to solve financial problems, detect spam, provide medical diagnoses, and other tasks. To build a perfect model, a lot of hand-labeled data is required which is not always readily available. To overcome this challenge companies are finding it easier to use weak supervision methods. This is a technique that combines inaccurate, limited, and lower quality data sources to build a robust indication model saving on cost, time, and challenge of getting hand-labeled data sets, therefore, increasing labeled data available for training. <\/p>\r\n<p>As described, weak supervision in machine learning can be a very effective method for working efficiently. One concept that captures the essence of this method is the concept of putting forth 40% of the effort but getting 90% of the results compared to a method that is mistake free. In other words, this method may sometimes be incorrect, but it is correct much more often than not, and it can be very efficient because it is allowed to make mistakes sometimes. For this reason, the methodology can be extremely efficient in simply getting things done. Weak supervision allows things that wouldn&#8217;t be possible if mistakes weren&#8217;t tolerated because the ruleset would otherwise be too complex and inefficient. A good example of when one may want to utilize weak supervision is when one has a large amount of unlabeled data and some mistakes with labeling are tolerated. Weak supervision would allow for the set of data to be quickly labelled and converted into something useful.<\/p>\r\n<p>Or, to summarize the benefits briefly: Weak supervision it is easy to implement, fast to run, and can be used for a variety of machine learning tasks. With weak supervision it is especially possible to create very many or large training datasets very quickly. This is the biggest advantage of weak supervision. Weak supervision works well when there are many unlabeled samples but few labeled samples. It also works well when the labels are uncertain or incomplete. Because sometimes systems can be better trained with a lot of not so well labeled data than with only a few very well labeled data.<\/p>\r\n<p>Weak supervision in machine learning is sometimes also used as a technique where the training data is not labeled. Instead, the labeling is done by a separate algorithm. This makes Weak Supervision an ideal technique for semi-supervised or unsupervised learning problems.<\/p>\r\n<p>In addition, it is also a benefit of weak supervision that it can be used for a variety of different machine learning tasks such as image recognition, text classification, and <a href=\"https:\/\/www.clickworker.com\/ai-glossary\/natural-language-processing\/\" title=\"natural language processing - term explanation\">natural language processing<\/a> (NLP).\r\n<\/p>\r\n \r\n \r\n<blockquote><p class=\"pb-3\"><strong>Tip:<\/strong><\/p>\r\n\r\n<p class=\"pb-3\">Do you need more hand-labeled data to properly train your AI system after all? Then use the Annotation Service by clickworker and let humans custom label your training data according to training requirements.<\/p><a href=\"https:\/\/www.clickworker.com\/image-annotation-services\/\" class=\"btn btn-primary\" role=\"button\">Image Annotation Service<\/a><\/blockquote>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"ml\"><strong>How can you use weak supervision for machine learning?<\/strong><\/h2>\r\n<p>Weak supervised learning is a technique for training machine learning models that uses a weaker form of supervision than strong supervised learning. This type of supervision allows the learner to make its own decisions about how to improve its performance, rather than having the decision made for it by the instructor. Weak supervised learning can be used in two ways: as an enhancement to traditional reinforcement learning, or as an alternative to reinforcement learning when no data is available.<\/p>\r\n<p>In traditional reinforcement learning, the learner receives rewards (rewards are usually tokens) whenever its predictions match actual values in the data set. In weak supervised learning, however, there are no explicit rewards; instead, the learner only gets feedback on its success rate (i.e., how often its predictions matched actual values). As long as this success rate remains high over time (i.e., as long as the learner does not get too far away from what we would consider good performance), then we call this approach &#8220;weak&#8221; because there is little or no punishment for incorrect predictions.<\/p>\r\n<p>This approach has several advantages over traditional reinforcement learning: firstly, it is more flexible because there are no hard-wired rules about how to get rewards; secondly, the learner can be more intelligent because it can figure out for itself what works best in a given scenario.<\/p>\r\n<p>Weak supervised learning has several applications in machine learning, including generalization (e.g., improving the performance of deep neural networks), bootstrapping (i.e., training a model without any data), and anomaly detection (i.e., detecting changes in data that may indicate an issue).<\/p>\r\n<p>Weak supervision in machine learning is used for training AI systems; natural language processing, which helps computers understand human communication; and image recognition, which identifies objects in photos or videos.<\/p>\r\n\r\n\r\n<h3>Find here a short list of typical uses of weak supervision:<\/h3>\r\n\r\n<ul>\r\n<li>Classification of text and documents<\/li>\r\n<li>Classification of structured data<\/li>\r\n<li>Classification of videos<\/li>\r\n<li>Cross-modal ad image classification<\/li>\r\n<li>Entity linking<\/li>\r\n<li>Rich document processing<\/li>\r\n<li>Utterance classification and conversational AI<\/li>\r\n<li>Information extraction from unstructured text, PDF, HTML and more<\/li>\r\n<li>Time series analysis<\/li>\r\n<\/ul>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"frontier\"><strong>Weak Supervision presents a new frontier in Machine Learning<\/strong><\/h2>\r\n<p>Weak supervision is a new paradigm in machine learning or artificial intelligence. By using high level noisy sources of labels, data can efficiently be trained into models for improved real-world performance. Weak supervision for machine learning can achieve state of the art scores on benchmarks designed to measure machine learning frameworks and their capabilities. The question is how to get more labeled training data more efficiently. Weak supervision for machine learning differs from active learning by being semi-supervised such that unlabeled data which is available cheaply and in larger data sets can be used. Generative models act as expressive vehicles with the ability to predict outcomes more reliably from large unlabeled datasets.<\/p>\r\n<p>Newer artificial intelligence frameworks are providing tools for programing with weak supervision in mind in order to accelerate machine learning progress. The next step is to massively multitask these protocols to generate labeling functions automatically from supervising tasks that include natural language and images. They will include the ability to fine tune granularity to make acceptable tasks more dynamic. The field is rapidly changing and so are weak supervision protocols. Machine learning can make large strides with the aid of these new techniques.<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"vs\"><strong>Weak Supervision vs. Rule-Based Classifiers<\/strong><\/h2>\r\n\r\n<p>Weak supervision is an approach to machine learning where we rely on just a few labeled examples (the supervised training data) to help train our unsupervised models more accurately. This improves the accuracy and generalization of our models without having to explicitly label every example.<\/p>\r\n<p>Rule-based classifiers are another type of machine learning model that relies on sets of rules instead of features or labels. These rules are used by the computer to make decisions automatically, based on what it has seen before.<br\/>\r\nWeak supervision and rule-based classifiers are both input-based methods. However, weak supervision uses human-provided subject matter expertise to create a set of training labels for multiple unlabeled data points. This method is more robust than a corresponding rule-based classifier.\r\n<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"methods\"><strong>What are some common weak supervision methods?<\/strong><\/h2>\r\n<p>Some common weak supervision methods are reinforcement learning, genetic algorithms, and artificial neural networks. Each method has its own strengths and weaknesses. You should choose a weak supervision method that best suits your data and goals.\r\nWeak supervision methods are used to learn from data without being explicitly told what to do. These methods use feedback mechanisms such as rewards or punishments to help the machine learn.<\/p>\r\n<p>The most common weak supervision method is reinforcement learning. In <a href=\"https:\/\/en.wikipedia.org\/wiki\/Reinforcement_learning\" rel=\"noopener\" target=\"_blank\">reinforcement learning<\/a>, machines learn by observing how well they perform in comparison with other instances of the same task that were supervised by a human expert.<\/p>\r\n<p>Reinforcement learning can be used for tasks such as navigation, image recognition, and text classification. Genetic algorithms are similar to reinforcement learning but they use mutations instead of rewards or punishments. This allows them to explore different solutions more quickly than traditional reinforcement learning techniques.<\/p>\r\n<p>Artificial neural networks (ANNs) are a type of machine intelligence that uses interconnected layers of neurons shaped like an onion skin. They&#8217;re often used for tasks such as natural language processing and object recognition.<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"types\"><strong>Types of weak supervision<\/strong><\/h2>\r\n<p>Some of the common weak supervision methods are: Incomplete supervision, Inaccurate and Inexact supervision. Incomplete supervision comprises a blend of a small domain expert labeled data set and unlabeled data to train a model. Active learning and semi-supervised learning are two ways in which one can deal with data set issues under Incomplete supervision. Inaccurate supervision is a weak supervision method where the training data available contains labels with errors. This is because the labeled data set, may not originate from field experts but is collected from the public or crowdsourcing datasets therefore some of the labels may contain mistakes or are wrong. Finally, we have inexact supervision, a form of weak supervision method where some of the labeled data provided are not presented exactly as desired. Developers, therefore, have to use various techniques to correct the weak labels. <\/p>\r\n\r\n<h3>An overview of weak supervision types<\/h3>\r\n<p>In total, there are four different types of weak supervision:<\/p>\r\n<ul>\r\n<li>incomplete,<\/li>\r\n<li>inaccurate,<\/li>\r\n<li>inexact and<\/li>\r\n<li>semi-supervision.<\/li>\r\n<\/ul>\r\n<p><strong>Short explanations of the types of weak supervision:<\/strong><\/p>\r\n<p><strong>Incomplete supervision<\/strong> is when the training data does not include all the information needed to learn the task.<\/p>\r\n<p><strong>Inaccurate supervision<\/strong> is when the training data is incorrect or contains errors.<\/p>\r\n<p><strong>Inexact supervision<\/strong> is when the labels in the training data are not precise. It uses multi-instance learning which allows for more accurate predictions by considering multiple instances of an object instead of just a single example.<\/p>\r\n<p><strong>Semi-supervision<\/strong> is when there is an incomplete set of labels and a desired output.<\/p>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"labels\"><strong>Types of Weak Labels<\/strong><\/h2>\r\n<p>There are different types of weak labels, and each one can be used for a specific purpose.<\/p>\r\n<p><strong>The four main types of weak labels are:<\/strong><\/p>\r\n<ul>\r\n<li>descriptive,<\/li>\r\n<li>interpretive,<\/li>\r\n<li>prescriptive, and<\/li>\r\n<li>evaluative<\/li>\r\n<\/ul>\r\n<p>Each type of weak label has its own advantages and disadvantages.<\/p>\r\n<p><strong>descriptive:<\/strong> Descriptive labels help you understand the data by describing it in detail. They&#8217;re useful for finding patterns and understanding how the data is related to other information.<\/p>\r\n<p><strong>interpretive:<\/strong> Interpretive labels help you make decisions by providing feedback on your interpretations of the data. They&#8217;re useful for making predictions or deciding what actions to take based on your observations.<\/p>\r\n<p><strong>prescriptive:<\/strong> Prescriptive labels tell you what should or shouldn&#8217;t be done with the data. They&#8217;re helpful for setting standards or directives about how to use the data.<\/p>\r\n<p><strong>evaluative:<\/strong> Evaluative labels provide feedback on how well someone performed using a certain technique or approach. They can also provide an overall evaluation of a person&#8217;s work performance<\/p>\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"best\"><strong>What are some best practices for using weak supervision?<\/strong><\/h2>\r\n<p>Weak supervision is a supervised learning algorithm that uses partially observed data.<\/p>\r\n<p>Best practices for using weak supervision include:<\/p>\r\n<ul>\r\n<li>Using a sparse representation of the target variable.<\/li>\r\n<li>Regularizing the error terms to improve generalization.<\/li>\r\n<li>Regularizing the weights to reduce bias.<\/li>\r\n<li>Minimizing cross validation variance by selecting appropriate subsets of data for training and testing.<\/li>\r\n<\/ul>\r\n\r\n\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"implement\"><strong>How do you implement weak supervision in your machine learning pipeline?<\/strong><\/h2>\r\n<p>Weak supervision can be used in machine learning pipelines to improve accuracy without having to rely on complete knowledge of the student&#8217;s errors. This allows you to use more data and train your models more accurately, which can result in better predictions.<\/p>\r\n<p>In weak supervision, the teacher (the &#8220;supervisor&#8221;) only has partial information about how well each student is doing. The supervisor uses this incomplete information to make decisions about how much weight to give each error prediction from a student model.<\/p>\r\n<p>This way, even if a particular student model contains many errors, it will still be given some weight when making decisions about whether or not to continue training that model. However, if a particular student model contains very few errors, its error predictions will have little impact on decision-making, and it will be less likely to be used for future training sessions.<\/p>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"challanges\"><strong>What are some common challenges with weak supervision?<\/strong><\/h2>\r\n<p>Weak supervision has several common challenges that include over-fitting, bias, and lack of generalization.<\/p>\r\n<p>Over-fitting occurs when the algorithm tries to learn from data that it doesn&#8217;t actually use or understand. This can cause the algorithm to become too specialized and unable to generalize from one set of data to another. Bias refers to how the model may be biased towards some types of inputs while ignoring others, which can lead to incorrect predictions. Lack of generalization means that the model can&rsquo;t make accurate predictions for new instances or situations that it hasn&#8217;t been trained on before. These problems can be difficult to overcome because they often result in inaccurate models and poor performance overall.<\/p>\r\n<p>There are several ways to overcome these challenges, and some of the most common include:<\/p>\r\n<ol>\r\n<li>establishing clear goals and objectives for your machine learning project.<\/li>\r\n<li>ensuring that you have a well-defined data set to train on.<\/li>\r\n<li>selecting an appropriate algorithm or model for your problem.<\/li>\r\n<li>choosing an effective training methodology.<\/li>\r\n<li>monitoring and correcting your machine learning models as they learn.<\/li>\r\n<\/ol>\r\n\r\n<h2 class=\"section-headline mb-4\" id=\"conclusion\"><strong>Conclusion<\/strong><\/h2>\r\n<p>Weak supervision can be a powerful tool for machine learning, but it is important to be aware of the potential problems that can arise. In particular, weak supervision can lead to over-fitting if not used correctly. However, when used correctly, weak supervision can provide labels for data that would otherwise be difficult or impossible to label.\r\n<\/p>\r\n\r\n\r\n\t<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/section>","protected":false},"excerpt":{"rendered":"<p>W Weak Supervision Weak Supervision \u2013 Short Explanation Saving time with weak supervision What are the benefits of weak supervision? How can you use weak supervision for machine learning? Weak Supervision presents a new frontier in Machine Learning Weak Supervision vs. Rule-Based Classifiers What are some common weak supervision methods? Types of weak supervision Types [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":54483,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"blank_with_breadcrumb.php","meta":{"footnotes":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What is Weak Supervision and how to use it for Machine Learning?<\/title>\n<meta name=\"description\" content=\"In this glossary post, we will discuss what weak supervision is, how to use it for machine learning, and some of the problems that can arise.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.clickworker.com\/ai-glossary\/weak-supervision\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" 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