What is Data Labeling? (Definition, Examples)

Data labeling refers to the way items in raw data are identified and given meaning so that machine learning models can use that data. Let’s say the raw data is pictures of animals. In that case, you should label all the different animals in your model, such as birds, horses, and rabbits. Without proper labels, machine learning models cannot recognize different data types in images.

Data labeling is an important step before training or using any machine learning model. It is involved in many applications such as computer vision, natural language processing (NLP), image and speech recognition.

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How does data labeling work?

There are two main categories of machine learning algorithms: supervised and unsupervised.

Supervised machine learning algorithms require that you provide labeled data to the algorithm to train it, and then apply what it learns to new data. The more accurate the labeled data, the better the algorithm’s results. Most often, labeling data starts with someone (often called a “labeler”) making some decisions on unlabeled data for the algorithm to learn from.

Let’s say we want our algorithm to identify trees. To train the model, the labeler is first presented with a photo and has to answer ‘true’ or ‘false’ indicating whether the image contains trees. The algorithm then uses these decisions to identify image patterns, learn what a tree is, and use it to predict whether future images will contain trees.

How data is labeled

Companies and developers take it very seriously because data labeling is essential to developing good machine learning models. However, labeling data can be time consuming, so some companies may outsource or automate the process with tools and services.

Various approaches can be used to label the data. Deciding between these approaches depends on the size of your data, the scope of your project, and the time required to complete the project. One way to classify different labeling methods is whether humans or computers do the labeling. Human labeling can take one of three forms.

internal labeling

This approach is used by large companies that have many dedicated data scientists working on labeling the data. Internal labeling is safer and more accurate than outsourcing because it is done in-house without sending data to external contractors or vendors. This approach prevents data from being leaked or misused if the outsourcing agent is untrusted.

outsourcing

This option is a good method for large, high-level projects that require more resources than your company can afford. That said, you still need to manage your freelance workflow, which can be expensive and time consuming. In such cases, companies hire different teams to work in parallel and complete the work on time. To maintain work flow and quality, all teams should use a similar approach in delivering results. Otherwise, more effort will be required to get the results in the same format.

crowdsourcing

In this approach, a company or developer uses a service to label data quickly and inexpensively. One of the most famous crowdsourcing platforms his reCAPTCHAwhich basically generates a CAPTCHA and asks the user to label the data.The program then compares results from different users and generates labeled data.

However, if labeling is automated and done using a computer, one of two methods can be used.

Synthetic label

This approach uses the original data to generate synthetic data to enhance the quality of the labeling process. This approach yields better results than programmatic labeling, but requires a large amount of computational power, as generating more data requires more processing power. This approach is suitable if you have access to supercomputers or computers that can process and generate vast amounts of data in a reasonable amount of time.

Programmatic labeling

To save computational power, this approach uses a script to perform the labeling process instead of generating more data. However, programmatic labeling often requires human annotation to ensure labeling quality.

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Advantages of data labeling:

Data labeling gives users, teams, and companies a better understanding of the data and its uses. Primarily, data labeling provides a way to provide more accurate predictions and improve data usability.

more accurate predictions

Accurate data labeling provides better quality assurance within machine learning algorithms than using unlabeled data. This means that the model is trained on higher quality data and gives the expected output. Properly labeled data ground truth (that is, how the labels reflect real-life scenarios) and iterate subsequent models.

Improved data usability

Data labeling also improves the usability of data variables in your model. For example, you can reclassify a categorical variable as binary to make it easier to use in your model. Aggregating data allows you to reduce the number of model variables or include control variables, thus optimizing your model. Whether you use the data to build computer vision or NLP models, using high-quality data is a top priority.

Drawbacks of data labeling

Data labeling is expensive, time consuming, and prone to human error.

expensive and time consuming

Data labeling is important for machine learning models, but can be costly in terms of both resources and time. Suppose your business takes a more automated approach. In that case, the engineering team needs to set up the data pipeline prior to data processing. Manual labeling is often expensive and time consuming.

prone to human error

These labeling approaches are also subject to human errors (coding errors, manual entry errors, etc.) that can degrade data quality. Even small errors can lead to inaccuracies in data processing and modeling. Quality assurance checks are essential to maintain data quality.

Best practices for labeling data

Regardless of the labeling approach you choose for your data labeling project, there are a set of best practices to improve the accuracy and efficiency of your data labeling process. For example, building machine learning models with large amounts of high-quality training data is expensive and time-consuming. To develop better training data, you can use one or more of the following methods.

• Consensus of Labelers Helps counteract individual labeler errors and unconscious biases. Errors can include incorrect labeling and double labeling of data. Additionally, one of the challenges in machine learning is when the data does not fully represent all possible labels, which introduces bias into the training data itself.
• label audit Keep your labels up to date and ensure accuracy. Often, once a machine learning database is built, it is regularly updated with new data that must be labeled before it can be stored and used. Auditing data ensures that new data is labeled correctly and that old data is relabeled to be consistent with their new labels.
• active learning Another machine learning approach is used to determine the small amounts of data that human labelers need to label or check. In Active Her Learning, a human labeler first labels a small amount of data and then uses these labels to train a model on how to label future data.

Examples of data labeling tools

There are many online tools and software packages available for labeling data using any of the above approaches.

1. Label Me is an open-source online tool that helps users build image databases for computer vision applications and research.
2. laziness is a free tool for labeling image and video files. One of its famous use cases is facial recognition.
3. Bella A tool used to label text data.
4. tag tag is a startup that provides a web tool of the same name for automatic text classification.
5. Prato is free software for labeling audio files.