The difference between labeled and unlabeled data

Data is an essential building block of artificial intelligence and data labeling is a key step in developing high-performing machine learning models. In short, data labeling enables these algorithms to build an accurate understanding of the world around us — and it's showing no signs of slowing down anytime soon.

As we move toward greater digitalization and automation in our day-to-day activities, data — and its proper classification — is becoming increasingly critical to our collective success.

If you'd like to find out more about labeled data and unlabeled data, you've come to the right place. In this post, we cover a variety of related topics to give you a high-level overview. To start off, we take a closer look at the basic idea of how labeled and unlabeled data are defined, then we go on to analyze the difference between labeled and unlabeled data, subsequently taking a deeper dive into labeled and unlabeled data in machine learning. Let's jump right in and get started.

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What is data labeling and how does it work?

Before we get into the weeds, it's important to have some background knowledge about how data labeling works. So, let's begin with the basics.

Under the umbrella of AI and computer science, machine learning uses data and algorithms to imitate the way humans learn, while gradually improving in accuracy. In machine learning, data labeling is the process of identifying raw data (images, text, videos, and so on) and adding one or more labels to provide context so that a model can learn from it. For example, labels help to identify the content of an image, speech in an audio recording, or what's shown on an x-ray.

To create a label, humans are asked to make judgments about a piece of unlabeled data. For example, they take a look at a picture (a data point) and answer the question: "Is this a picture of a cat or a dog?"

These labels serve a vital function in helping machine learning models make the best predictions — just as their human counterparts who are responsible for creating, training, fine-tuning, and testing these models. Ultimately, data annotators help guide the data labeling process by creating labeled datasets that are most relevant to a particular project.

What’s the difference between labeled and unlabeled data?

Before we dive into the question at hand, let's first start off by defining labeled data and unlabeled data.

So, labeled vs unlabeled data — what's the difference?

• Labeled data contains meaningful tags and is used in supervised learning, while unlabeled data doesn’t contain additional information and is used in unsupervised learning.
• Labeled data requires the additional process of labeling, while unlabeled data is essentially raw data before labeling.
• Labeled data is harder to obtain (there are less datasets available, or you have to label it yourself), whereas unlabeled data is more abundant.

Now, let's dive into the details.

Labeled data

With the help of human annotators, labeled data enhances a set of unlabeled data with meaningful tags, labels, or classes. Once a labeled dataset is created, a machine learning model can be fed this labeled dataset so that when it encounters new unlabeled data, it can accurately predict and assign an appropriate label to that data.

• Labeled data is used in supervised machine learning — a machine learning approach in which labeled datasets are used to train or "supervise" a machine learning algorithm in categorizing data or making accurate predictions (the model can measure its accuracy and learn over time by using labeled inputs and outputs).
• It’s harder to obtain and store (can be time consuming and costly).
• It can be used to identify actionable insights, such as predictions.

Supervised learning can further be broken down into two subsets:

Classification: Using algorithms to correctly assign test data to specific categories, such as separating junk mail from your inbox.

Regression: Using algorithms to understand the relationship between dependent and independent variables and forecasting numbers based on different data points, such as sales revenue projections.

Unlabeled data

Unlabeled data on the other hand, doesn't have any meaningful tags or labels and usually consists of natural or human-created samples such as photos, audio recordings, videos, news articles, tweets, or x-rays that can be easily obtained.

Computers use labeled and unlabeled data to train machine learning models, but what's the difference?

• Unlabeled data is used in unsupervised machine learning — applies ML algorithms to analyze and cluster unlabeled data sets by uncovering patterns without the help of humans.
• It’s easier to obtain and store.
• It doesn't have as many uses (however, unsupervised learning methods can help uncover new data clusters for additional categories).

Unsupervised learning models are used for three main tasks:

Clustering: Grouping unlabelled data based on similarities or differences, as seen in market segmentation, image compression, etc.

Association: Using different rules to find relationships between variables in a dataset, as used in market basket analysis and product recommendations.

Dimensionality reduction: Applied when the number of features (or dimensions) in a dataset is too high in the preprocessing data stage, such as improving image quality by removing noise.

Breaking down the learning patterns further

Wondering what the main difference is between supervised and unsupervised learning? Labeled data. To put it briefly, supervised learning uses labeled input and output data, whereas unsupervised learning algorithms do not.

It's also helpful to note that unsupervised learning has more complex algorithms since we don't know much about the data or anticipated outcomes. With a smaller number of models and fewer ways to check for accuracy, unsupervised learning creates a less controlled environment since the machine is generating outcomes for us.

On the other hand, semi-supervised learning combines unlabeled and labeled data (or sets of unlabeled data where only a few pieces of data have labels) into integrated models. There's a lot of research in this area on ways to build better and more accurate real-world models.

The third basic machine learning technique, reinforcement learning, enables a model to learn in an interactive environment by trial and error using feedback from the environment.

Reinforcement learning is often associated with the Markov Decision Process (MDP), which provides a mathematical framework for modeling decision making in situations where outcomes are partially random. It's often used for studying optimization problems to learn from an interaction and achieve a specific goal.

Reinforcement versus supervised and unsupervised learning: What's the difference?

So, how does reinforcement learning differ from supervised and unsupervised learning?

Unlike supervised learning where the feedback provided is based on a correct set of actions for performing a task, reinforcement learning doesn't require any labeled input or output pairings, nor does it need any actions to be corrected. Instead, reinforcement learning uses rewards and penalties to indicate positive and negative actions.

Compared to unsupervised learning, reinforcement learning has a different objective: to find a suitable action model that maximizes the total cumulative reward — whereas the aim of unsupervised learning is to find similarities and differences between pieces of data.

How can labeled and unlabeled data be used?

Now that we have a clearer understanding of the differences between labeled data and unlabeled data, let's look at how they can be used. Because of their differences, some machine learning algorithms can only work with a labeled dataset while others can only work with unlabeled data.

This depends on several factors including the type of task, the primary aim of the task, the availability of data, the degree of general versus specific knowledge needed to carry out the required data annotation and tagging, and the overall complexity of the decision-making process.

As mentioned, labeled data corresponds to regression and classification tasks, which fall under the category called supervised learning. These include:

• Predicting unseen values.
• Mapping the relationship between two variables.
• Testing scientific hypotheses.
• Entity recognition via computer vision and speech-to-text systems.

Whereas unlabeled data is associated with clustering and dimensionality reduction tasks, which fall under the category called unsupervised learning. These include:

• Identifying subsets of observations that share common characteristics.
• Decreasing the complexity of a dataset to reduce the resources needed to process it.
• Standardizing a dataset to train neural networks (known as feature scaling).

Note: a neural network is a specific type of machine learning model that teaches computers to process data in a way that resembles the human brain.

Unlabeled data used in unsupervised learning extracts insights based solely on the quantitative characteristics of datasets. Since it requires little previous knowledge, the objectives aren't that complex — they may include:

• Reducing the dimensionality of a dataset to limit the resources needed to train neural networks.
• Developing a neural network that encodes a dataset into a higher abstract representation (known as an "autoencoder").

Supervised learning often has more varied goals, which might include:

• Recognizing objects in images.
• Predicting the value of stocks.

Types of data labeling in machine learning models

Data labeling has a lot of different uses. Many have to do with computer vision, natural language processing, speech recognition, and audio processing. Let's take a closer look at these three main categories.

Computer Vision allows applicable systems to obtain data from digital input like images and videos, then take action or make recommendations according to that input.

Data labeling is the beginning phase of generating a training dataset. Some initial tasks could include labeling images or key points. You could also use a bounding box — an enclosed border — to select an object or group similar elements in an image.

Another option is to categorize images by type or content. You can also segregate by pixels. From there, this training dataset serves as the foundation for a computer vision model, which has many uses such as classifying images, uncovering the whereabouts of objects, determining key points, and more.

Natural Language Processing aims to build machines that are able to comprehend and respond to written or spoken words in the same way that humans are able to.

To create a training dataset, either pinpoint the primary parts of text you want to highlight or use tags with distinct labels. Examples include determining parts of speech, classifying individual names and locations, or discerning text in images.

To do this, you can use bounding boxes to outline the text and copy the written words into text format. Some use cases for these models include optical character recognition, entity name recognition, and sentiment analysis.

Audio Processing is central to recording, enhancing, storing, and transmitting audio content.

Also used to remove unwanted noise, add effects, boost frequency ranges, and more, this process converts different sounds such as dialects, construction or animal noises into recognizable patterns that can be incorporated into machine learning. You'll need to describe and write out these sounds (for example, a dog barking, a bird chirping, or an alarm going off). Then, you can delve deeper by adding tags and classifying the auditory parts — this serves as your training dataset.

Best ways to label data

You can make data labeling better, faster, and more precise in several ways, such as:

• Developing insightful and efficient task interfaces for the people who will be classifying your data. The more streamlined your labeling process is, the more efficient your labeling efforts are. This is especially noticeable when labeling huge amounts of data.
• Utilizing various methods of aggregating data to avoid errors and offset personal biases. Creating agreement among labelers can be done by collecting and consolidating feedback from many people into a single label.
• Evaluating labels to check for correctness by giving evaluation tasks to a different group of labelers. They can check the correctness of the initial labeling performed by other people.
• Applying active learning to determine which additional data needs to be labeled.

How can we streamline data labeling?

To build high-performing machine learning models, you need high quality data. Getting ahold of this data can be costly, complex, and inefficient. Labels created by people are needed for the majority of models to help them generate the correct predictions. To help streamline and automate this process, you can apply a machine learning model to label the data directly.

Firstly, a machine learning model is trained on a subset of raw training data that has already been labeled by humans. A model with a track record of producing precise outcomes from the information that it has learned thus far, can add labels to unlabeled data automatically. A less accurate model requires human annotators to add labels. Labels created by humans enable the model to learn and enhance its capacity to categorize new data.

Eventually, the model is able to label an increasing amount of data automatically and speed up the creation of training datasets. Of course, implementing quality control in such models is also a necessity, as with time it might drift and start producing less accurate results. In this case, human annotators can step in again.

Internal labeling (in-house), synthetic labeling (generating new data from previous datasets), programmatic labeling (using scripts), outsourcing (or freelancing) constitute a variety of data labeling methods. However, our favorite is obviously crowdsourcing — a great way to outsource data labeling and get around the drawn-out and expensive management processes. Check out our data labeling platform to learn more!

To sum up…

At its basics, data labeling provides a way to categorize data by assigning an appropriate tag or label to raw data — examples of which include pictures, written words, as well as video and audio recordings.

Data labeling gives meaning and context for machine learning models, which apply this data to generate better and more exact predictions.

There are a lot of uses for labeled data across computer vision, natural language processing, and speech recognition. Countless companies across industries combine the software, processes, and human efforts of data scientists and annotators to sort, categorize, and label data — which essentially turns into a training dataset for machine learning models.

As alluded to earlier, data labeling can either be carried out manually (by a human) or automatically (by a machine) — both have pros and cons. Manual labeling by humans is rather expensive in both the financial and time sense, but crowdsourcing provides a great alternative.

By using a crowdsourcing platform like Toloka for data labeling tasks, you can successfully tap into the wisdom of the crowd on a large scale — or earn some extra cash doing fun micro tasks as a fellow Toloker. With countless annotators around the world carrying out tasks posted by AI teams and businesses alike, our platform gives individuals and corporations alike the necessary tools to oversee data labeling quality and construct a streamlined pipeline for their machine learning tasks.

To get up to speed on all things related to data labeling, machine learning, and AI, we invite you to visit our blog.

About Toloka

Toloka is a European company based in Amsterdam, the Netherlands that provides data for Generative AI development. Toloka empowers businesses to build high quality, safe, and responsible AI. We are the trusted data partner for all stages of AI development from training to evaluation. Toloka has over a decade of experience supporting clients with its unique methodology and optimal combination of machine learning technology and human expertise, offering the highest quality and scalability in the market.