Introduction to Labeled Data: What, Why, and How

What Is Labeled Data?

As the name suggests, labeled data (aka annotated data) is when you put meaningful labels, add tags, or assign classes to the raw data that you've collected. What is a label in machine learning? Let’s say you are building an image recognition system and have already collected several thousand photographs. Labels would be telling the AI that the photos contain a ‘person’, a ‘tree’, a ‘car’, and so on.

The machine learning features and labels are assigned by human experts, and the level of needed expertise may vary. In the example above, you don't need highly specialized personnel to label the photos. However, if you have, say, a set of x-rays and need to train the AI to look for tumors, it's likely you will need clinicians to work as data annotators. Naturally, due to the human resources necessary, hand-labeling data is much more expensive than gathering raw unlabeled data.

What Is Labeled and Unlabeled Data in Machine Learning?

We've already discussed the major differences of labeled vs unlabeled data in our previous article but it won't hurt to remind the basics.

UNLABELED DATA	LABELED DATA
Used in unsupervised machine learning	Used in supervised machine learning
Obtained by observing and collecting	Needs human to label
Comparatively easy to get and store	Expensive, hard and time-consuming to get and store
Often used to preprocess sets of data	Used for complex predicting tasks

So we can see that, while labeled data is expensive and hard to get, it also offers a much wider array of possibilities. In a battle of labeled vs unlabeled data, the former ipso facto wins. Surely, you can (and should) use unlabeled data to train your AI. But you'll miss quite a lot of potential if you don't label it next.

What Can Labeled Data Do?

Labeled data makes the training process much more efficient and simple. The idea behind labeling data is to teach the AI to recognize patterns according to the task or target. This way, after the training process, the input of new unlabeled data will lead to predictable labels. You add labels to data and set a target, and the AI learns by example. The process of assigning the target labels is what we know as annotation Click to Tweet

To put it simply, this means that you add labels to data and set a target, and the AI learns by example. The process of assigning the target labels is what we know as annotation. After the training period ends, your machine will be able to identify the presence of a 'person', a 'car', or a 'tree' in the new photos. Not only that but the AI trained on labeled data can be used for complex forecasting (e.g., predicting the prices on the stock market or suggesting additional products for the customer). Sounds fascinating, right?

Labeled Data in Machine Learning Models?

Labeled data is used mostly in supervised learning but also semi-supervised learning, in combination with unlabeled data. Let's take a deeper look into both of these types. We've included a bunch of examples to better explain the utility of labeled data.

Supervised Learning: Label Your Data

According to AltexSoft, "supervised machine learning entails training a predictive model on historical data with predefined target answers". Your set of labeled data is used to teach the machine with a specific goal in mind. For a set of labeled emails, for example, you might need the AI to recognize patterns to predict buying behaviors.

Supervised learning (SL) is using machine language to classify and process data. According to these two major types of tasks, there are two main groups of methods in SL.

Classification

When you need the machine to tell the class of the data that you input, you use classification to train it. This group of methods are based on the analysis of data and spotting patterns in order to get qualitative responses (e.g. if a letter is spam or no, or if a photo has a car in it or a bicycle). While unlabeled data allows clustering the photos into groups, it cannot tell what those groups are. Labeled data used in supervised learning, on the other hand, trains AI not only to recognize different objects on the photos but also to tell what classes those objects belong to.

Now let's see how this works in practice. If you have a set of 10.000 emails labeled with 'spam' or 'not spam', classification trains your model to tell if the letters are spam. When the training is over, newly input letters will be classified into one of these two groups. The input element can belong to several categories at once. When you train your AI on a set of photos tagged with 'person', 'car', 'tree', and 'house', it will be able to tell which objects are present on the new photos you add to the set. In addition, the model will tell you how confident it is about these predictions.

Linear Regression

Linear regression (sometimes referred as reduction) methods take a different approach. They are used to recognize relationships in quantitative data by producing numerical values. E.g. within the example of the 10.000 emails, linear regression can answer the question if there is a relationship between additional marketing expenses and click-through rates (CTRs), and how strong it is.

Similarly to classification, regression problems usually have more than one input variable that impacts the target outcome. In addition to marketing expenses, the CTRs can be influenced by an increased number of products/services, a growing number of customers, etc. Such a type of regression is known as multiple linear regression.

Semi-Supervised Learning: Combining Labeled and Unlabeled Data

Obviously, labeled data is exactly what you need. However, you don't always have enough of it to train your AI. It is expensive and time-consuming to label thousands of images or emails, even more so if your field is highly specialized and requires highly skilled professionals to do the job. So what do you do?

Work with what you have. Combine the labeled data with unlabeled, an approach to machine learning known as semi-supervised learning. For these types of models, you don't need all of your data labeled; you just need certain data points. Semi-supervised learning allows you to use a small batch of labeled data to train your AI, and then apply this to the rest of the data that has no labels yet. Usually, you will need only around a quarter of your data labeled to build an effective semi-supervised model. As an outcome, you will get a big set of pseudo-labeled data.

Now let's return to that image recognition system you were supposedly building. You have 10.000 photos, only 2.500 of which are annotated with the labels 'car', 'house', 'person', etc. First, you use clustering on all your data to group it. Then you train the model on the labeled data. Afterward, you can maximize the effect on the rest of the batch to make sense of these clusters.

How to Label Data for Machine Learning?

Now that we know how to use labeled data, it’s time to ask another question: what is data labeling in machine learning as a process? The annotation starts with setting your goal and preparing the data. Then you choose between annotating all of it or a small sample that you'll use as an AI training ground in a semi-supervised learning model.

In order to label data, you need specialized software that allows adding labels to certain highlighted areas. You should choose an annotation method in accordance with your problem and the type of collected data (images, text, video, or audio). Here are a few of the most popular services:

• Polygon annotation
• Polylines annotation
• Semantic segmentation
• Bounding boxes
• Landmark annotation
• 3D cuboid annotation

For more information on what we offer at Label Your Data, visit our Services page.

Know Your Options: Where to Get Annotation?

Keep in mind that the process of annotation is time-consuming and expensive. Let's take a look at the ways that you can go to label your data, their benefits and drawbacks, and the situations, for which each of these can be the best solution.

Options	Benefits	Drawbacks	Ideal for
In-House	Control Adequate accuracy and quality	Expensive Time-consuming Requires training the team of labelers	Big companies with the constant need for data annotation
Outsource	High quality guaranteed Adequate accuracy and quality	Lack of control	Short-time or small projects. Long-term partnerships for seasonal or periodic projects.
Crowdsource	Very cost-effective Fast Flexible	Doubtful quality Inconsistent annotation tools Lack of control Risk of missed deadlines	Small businesses that lack resources but can be more flexible with quality demands
Synthetic	Time-effective Large volumes of new data High quality No human factor	Very expensive Requires a lot of computational power	Hi-tech companies with very strict deadlines
Programmatic	Time-effective Automated	Poor quality You need a team of data reviewers	Businesses with QA teams and the opportunity to check for mistakes
Transfer learning	Time-effective	Finding team willing to share Defining elements useful for a new problem	Training neural networks

In-House

As the name suggests, this option requires you to build your own team of data professionals. It's a great approach if you are a big company that has a permanent flow of unlabeled data that needs annotation. The major benefit of this approach is that you have a lot of control over your team, as well as the annotation process. This ensures not only timeliness but also the quality of performance. However, you will need to allocate resources for the team of annotators, which is not always a viable option.

Outsource

Another common approach is to look for a company that specializes in data labeling and can ensure the quality and professional approach. The external group will not give you the fullest control over your project but will take a load off your shoulders instead and allow you to focus on strategic tasks instead. This is a great alternative for smaller companies or short-time projects. Also, if you know you'll be coming back from time to time with new batches of data, outsourcing gives you the opportunity to form long-term partnerships with added benefits of trust and improved communication. If you don't have a constant need to label new data, or cannot handle a whole in-house team of data experts, why not ask us for a quote? ;)

Crowdsource

There are multiple platforms that allow you to enlist people from all over the world to work on your project. A huge appeal of this approach is its speed and flexibility: freelance data labelers take the tasks as soon as they become available. Besides, they are also quite cost-effective due to the high competitiveness of the field. On the other hand, mind the risk of missed deadlines and the inconsistency of annotation tools that vary from one platform to the other. In addition, these platforms cannot guarantee the high quality of the outsourcing option nor give the control that you have over the in-house team. Still, crowdsourcing a data labeling project is a good option for a small business that doesn't have enough resources but is flexible with quality demands and the use of annotation tools.

Synthetic

Aside from using human labelers, it's possible to generate new data by synthetic labeling. A popular way to do so is by using generative adversarial networks, or GANs. These create extremely realistic fake data with the necessary attributes from pre-existing sets of unlabeled data. We mentioned GANs in the article on unlabeled data , follow the link to learn more. The synthetic annotation approach is very time-effective since it doesn't require building a team of labelers. However, it still needs a lot of computational powers, which means this option is quite costly. You should consider synthetic labeling in case you are a hi-tech company that already has computational resources and time on your projects is of utmost importance.

Programmatic

Another option that doesn't require hiring data labelers is programmatic annotation. This option relies on the scripts and codes of machine learning that label data automatically. This means two things: first, it's time-effective, and second, it lacks quality. The latter specifically leads to the necessity of having not data labelers but data reviewers, a team of experts who will check the labeled data as it is being annotated by computers. Still, it's a good option if you have a quality assurance team, as well as room for checking mistakes.

Transfer Learning

Finally, yet another option to skip the need of hiring people to do the data labeling. Transfer learning allows repurposing the labeled data that was used for training AI in other projects. There are a lot of similar machine learning problems, so it makes sense to take the labeled data from other annotation teams. This way, you save time and human efforts. But the problem with this approach is finding the team willing to share their dataset and defining the essential elements that you can appropriate for your problem. Transfer learning is a great option for training neural networks (e.g. image recognition systems, human motion models, image segmentation, etc.).

So these are your choices. Most companies usually choose between building an in-house team or outsourcing the annotation process, a comparison that we've covered in our article on outsourcing. To make the best decision, look at the resources at your disposal, consider the requirements of your project, and choose the option that suits you best.

Did You Have Your Data Labeled Yet?

Without a doubt, unlabeled data can be useful. But it can only be used for a limited number of tasks. If you want to train your AI properly, you cannot do without labeled data.

Annotating data opens a lot of opportunities and solutions. First and foremost, labeled data is used in supervised machine learning. The methods of classification and regression help to solve problems in the areas from bioinformatics (think fingerprint or facial recognition in modern smartphones) to spam detection to customer recommendation engines. Still, hand-labeled data is hard to get and expensive to store. For this reason, the semi-supervised learning models gain popularity today. The combination of unsupervised learning methods such as clustering and supervised learning used to train AI and maximize its effect on the greater sets of data provides flexibility while saving resources and time. Look at the face recognition or speech analysis systems for the practical use cases of semi-supervised learning.

So how do you get annotated data? Well, you can do it yourself by training an in-house team or spending a handsome sum on computational power to do the job for you. Or you can transfer this problem to us! We'll take the burden of planning, training annotators, and control off your shoulders and let you concentrate on more strategic tasks. On our side, we guarantee you high quality and security of your data, and the results delivered right on time. Ready to strike a deal? Ask us for a quote!