What is Labeled Data in Machine Learning [Complete Guide]

When it comes to machine learning models, labeled data is one of the most important concepts.

From ML model training to making sure the models make correct predictions, it boils down to labeled data.

At this stage, it is natural to ask questions like:

• What does labeled data represent?
• What’s the difference between labeled and unlabeled data?
• What are the different approaches to data labeling?
• What are the use cases of labeled data?

If all this sounds confusing to you, I have got you covered!

Keep reading to learn everything you need to know about labeled data and its significance in data annotation.

Cool. So, let’s dive into this topic.

So, What Actually Is Labeled Data?

A labeled dataset is created by assigning meaningful labels to each data point in a raw dataset.

In other words, once the data labeling (or data annotation) process is complete, the resulting output is known as labeled data.

After labels or tags have been added, this dataset can be used to train machine learning models. 

The bottom line is this: labeled data contains useful information and is key to AI models making accurate predictions on completely new datasets.

What’s the Significance of Data Labeling in Machine Learning?

Now the question that arises is this: “What is the importance of data labeling for a supervised ML model?”

Just stick with me, as I will explain the key benefits of labeled data:

1. Used to Train Supervised ML Models

Supervised machine learning algorithms need a labeled training dataset to learn from. 

Without the labels, the model doesn’t know what outputs it’s supposed to predict, so it can’t learn the relationship between inputs and results…makes sense, right?

Only by training a supervised model on labeled data can it make accurate predictions on new, unseen data.

2. Improves the Accuracy of ML Algorithms

At this point, it may sound repetitive, but it’s worth mentioning again: in supervised learning, model accuracy is linked to the quality of labeled data.

When labels are accurate and consistent, machine learning models learn better patterns. And this means more accurate predictions.

High-Quality Data Labeling =  Better Model Performance

3. Saves You Time and Money

Here’s the deal: well-labeled data reduces errors and speeds up the development of AI models. 

Why? Because when the data is labeled correctly from the start, models learn faster and require fewer rounds of correction or retraining…saving both time and budget.

You can also use automation or AI-based annotation tools to generate labeled data more quickly and at scale, which further helps your business save time and money.

4. Helps You Make Informed Decisions

Accurately labeled data results in reliable machine learning models. And that, in turn, helps you make better decisions based on their predictions.

Keep this in mind: you can only rely on the results of a supervised learning model if it is trained on high-quality labeled data. Otherwise, the predictions could be flawed.

In other words, if the output of an ML model is accurate, it will help you make the correct decisions for your business.

See how all this is linked to data labeling?

Labeled Data vs Unlabeled Data: What’s the Difference?

In this guide, I used the term labeled data repetitively, as well as its importance.

But what about unlabeled data? How is it collected, and what’s its role in ML model training?

To help you get a clear picture, I have underlined the differences between labeled and unlabeled data in this table:

I hope this table helped you settle the labeled vs unlabeled data question.

Which Approach To Choose For Data Labeling?

Here’s the thing: there’s more than one way to label a dataset, and each approach comes with its own pros and cons.

Before deciding which option works best for you, let’s take a closer look at the most common data labeling approaches and what each one involves.

1. In-House Labeling

In-house labeling involves hiring and managing your own team of annotators to handle data labeling tasks internally.

While this approach gives you full control over the annotation process, it can be expensive and time-consuming. 

This is because recruiting skilled annotators, providing training, and managing day-to-day operations often require significant resources.

2. Outsourcing

This is the best data labeling approach, in my opinion.

Here, instead of managing an in-house team, a business owner simply contacts an outsourced team of annotators.

This is very budget-friendly, so it’s considered ideal, especially for small to medium-sized businesses.

By the way, while we are on this topic, HiredSupport offers high-quality data labeling services starting from just $7.

3. AI-Assisted Labeling

If you are looking for speedy work, manual data labeling might not be the best choice for you. 

In such cases, using an already trained AI model, you can automatically assign labels to the raw dataset.

But don’t forget that human review and verification are also needed in such cases.

Looking to Outsource Data Labeling Projects? Try HiredSupport

Managing all data labeling tasks in-house? It can all become overwhelming very quickly (I know it does!)

This is why you need to get in touch with a proven BPO company…such as HiredSupport.

With our dedicated team of data annotation specialists, we guarantee that your datasets are labeled quickly, efficiently, and accurately.

There is a reason why more than 100+ businesses trust us!

And just to let you know, we offer data annotation services from just $7, the most affordable rate!

Final Thoughts

To properly train your AI model, labeled data is the most important component.

And if you are looking to outsource your data annotation projects, you must consider HiredSupport.

Whether you are looking for image annotation, video annotation, or text annotation, our annotators are always here to help you.

So, instead of stressing over data labeling, get in touch with our team today and let us handle everything for you.