We live in a world where machine learning-powered applications play an increasingly important role across various industries. From computer vision and medical diagnosis to AI chatbots, machine learning models can classify data and make accurate predictions.
But have you ever wondered how these models learn to distinguish one type of data from another? The answer lies in labeled data.
Annotators assign meaningful labels to raw data, which is then used to train machine learning models. In other words, high-quality labeled data forms the foundation of many machine learning applications.
Keep reading to learn what is labeled data, how it is created, and why it plays such an important role in machine learning models.
What is Labeled Data?
In machine learning, labeled data is a dataset in which every data point is paired with a meaningful label: a label that correctly identifies its category or class.
In most cases, human annotators (or even AI-assisted annotation tools) assign labels to raw data. The resulting labeled dataset is then used to train machine learning models to recognize patterns and make accurate predictions.
Example:
Suppose you’re training an image classification model to distinguish between cats and dogs. In this case, the raw data you have collected consists of thousands of images of cats and dogs.
First, the annotators must assign labels to each data point. Each image is assigned a label such as “Cat” or “Dog”. By learning from these labeled examples and recognizing patterns, the model can then classify new images it has never seen before.
This example shows how accurately labeled data is fundamental to supervised learning models to make correct predictions.
Note: Don’t confuse labeled data with data labeling. Data labeling is the process of assigning meaningful labels to raw or unlabeled data, whereas labeled data is the final result of that process.
How is Labeled Data Used in Machine Learning Models?
Suppose your task is to build an ML model that can accurately identify cat images and distinguish them from any other object. So, how do you create labeled data and train the model?
Step 1: Data Collection and Preparation
The first step is to collect relevant images from online platforms or public datasets. Then, these unlabeled images are organized into a raw dataset. You can think of this step as the “pre-labeling stage”.
Step 2: Converting Raw Data into Labeled Data
Next, human annotators assign a label to each image. For this task, you can also rely on automated annotation tools when dealing with a large volume of raw data.
For example, an image containing a cat is labeled “Cat,” while an image of a dog is labeled “Dog.” In simple words, this is how raw data is converted into a “labeled dataset”.
Note: Accurately labeled data at this stage is one key factor leading to high-performing AI models.
Step 3: Model Training
In this phase, the finalized labeled dataset is used to train the machine learning model. The model learns the relationship between the input data (images) and their corresponding labels.
This is how ML models recognize patterns and distinguish between different classes, i.e cats or dogs.
Note: Labeled data is essential for training supervised learning models. But unsupervised learning models can learn from unlabeled data and don’t rely on annotated data.
Step 4: Evaluation and Testing
There’s one last step before you deploy the AI model. Here, the trained model is evaluated using a “separate set of unseen images.” This set of unseen data is known as testing data.
The testing phase is important to know whether or not the model can accurately classify new images (based on the patterns it learned during training).
4 Common Types of Labeled Data
A labeled dataset can be classified based on the type of data it contains. Let’s discuss the most common types of labeled data you might come across.
1. Text Data
This type of dataset contains textual content such as emails, documents, social media posts, and customer reviews.
2. Image Data
It includes visual content such as photographs, satellite images, medical scans, and social media graphics.
3. Audio Data
Audio data consists of sound-based content such as MP3 files, voice commands, songs, and podcasts.
4. Video Data
Any sequence of images in motion is categorized as video data. For example, surveillance footage, movies, and videos shared on social media platforms.
The job of annotators is to analyze these datasets and assign accurate labels that can be used to train supervised machine learning models.
What’s the Importance of Labeled Data for ML-Based Applications?
Here are some key factors that make labeled data so useful for machine learning applications.
1. Helps ML Models Identify Patterns
For a machine learning model to make accurate predictions, it learns by understanding the relationship between each data point and its corresponding label. This requires high-quality labeled data.
For example:
- An image labeled as a “dog” helps the model learn the features of a dog.
- An image labeled as a “human” helps the model learn the features of a human.
2. Improves the Model Prediction
By now, it’s clear that labeled training data is essential for an ML model to make predictions. But one question remains: how does the quality of labeled data affect model performance?
The answer is simple: the better the labeled data, the more accurate the model becomes. This is because high-quality datasets improve a model’s ability to generalize to unseen data.
In other words, accurately labeled data enable AI models to better recognize patterns from unseen data.
3. Reduces Data Bias
Data bias occurs when a dataset is unbalanced, inconsistent, or contains incorrect labeling. This negatively affects the accuracy of an ML model.
However, with high-quality data labeling, your annotation team can minimize data bias, leading to building more accurate models.
4. Forms the Foundation of Supervised Learning
The supervised learning algorithm depends on labeled training data. You must train the model using a labeled dataset so it can make accurate predictions for real-life scenarios. This is the reason why data labeling is a key part of any accurate supervised learning model.
On the other hand, unsupervised learning algorithms do not require labeled training data. Instead, they analyze unlabeled data to discover hidden patterns, relationships, or clusters without predefined labels.
What’s the Difference Between Labeled & Unlabeled Data in Machine Learning?
The following table will help you differentiate between the two types of datasets:
| Factor | Labeled Data | Unlabeled Data |
| How it Works | Raw data is labeled by annotators and used to train machine learning models. | No labeling is required. The model identifies patterns and relationships within the data. |
| Model Accuracy | Generally high | Depends on the learning model used |
| Cost | High | Comparatively lower |
| Pros | -> Suitable for supervised learning -> Generally more accurate -> Easier to evaluate results | -> Lower annotation costs -> Fewer human resources needed -> Readily available datasets |
| Cons | -> Expensive -> Time-consuming | -> Accuracy varies depending on the use case -> Results are sometimes harder to understand for naive users |
| Role of Annotators | High | Generally Lower |
| Ideal For | Classification and object detection | Clustering and pattern detection |
How Annotation Can Help You With Data Labeling
If you’re struggling with large-scale data annotation, outsourcing your data labeling tasks can be an ideal solution.
At Annotation, we provide high-quality data labeling services that help businesses, like yours, build accurate ML models.
Whether you need text data labeling, image data annotation, or custom annotation services, we can help with projects of any size or complexity; our data labeling services start at just $5 per annotation hour.
Fill out the form, and let our experts handle your data labeling projects.