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Labeled vs unlabeled data: everything you need to know

What is the difference between labeled and unlabeled data? Understanding the differences is a groundbreaking factor in the area of technological advancement. As AI survives on vast amounts of data, categorizing it helps transfer actionable intelligence for the model to learn and develop. In this article, we have dived deeper into the exploration of labeled vs unlabeled data covering everything you need to know to navigate the complexities of machine learning models and AI development. 

Alexey Kornilov Avatar

What is labeling and how it works

Labeling is attributing labels to raw data such as images, text or video. For instance, in a set of images, each photo might be tagged with labels indicating whether it contains a cat, a dog, or a car.

The primary goal of labeling is to provide a clear, understandable context to data that algorithms can then use to learn, predict, and make decisions.

It works by employing a combination of human intelligence and sometimes semi-automated tools to accurately tag vast amounts of datasets. 

The labeled data serves as a training set for machine learning models, teaching them to recognize patterns, objects, and scenarios without human intervention. Over time, as the model is exposed to more labeled data, its accuracy and efficiency in performing tasks like image recognition, natural language processing, and predictive analysis improve significantly.

How is labeled data created?

Labeled data is created by analyzing each piece of the raw data (image, video, text) and tagging them with labels descriptive of their characteristics. The process is mostly realized with the help of human annotators who use common intelligence to tag the information based on predefined categories. For example, in a photo, each image might be labeled as ‘dog’, ‘cat’, or ‘tree’ to help machines understand what the picture represents.

Additionally, there are labeling automation tools that can also assist in this process, using algorithms to propose labels that are then verified or corrected by humans.

Applications and examples of labeled data

Labeled data is applied in various fields, improving the capabilities of machine learning models to perform tasks that require understanding complex patterns.

In healthcare, labeled images of X-rays or MRI scans help in diagnosing diseases by training models to recognize signs of specific conditions.

In retail, labeled customer insights enables personalized marketing strategies by identifying shopping patterns.

In the automotive industry, autonomous vehicles rely on labeled data from road images and sensor information to navigate safely. 

Supervised learning models

Supervised learning models are a set of machine learning algorithms that are trained on pre-labeled data. There is an initial setup for further labeling which allows the models to learn the relationship between inputs and outputs, resulting in accurate predictions for new, unseen data.
There are two main types of supervised learning:

  1. Classification models predict discrete labels (e.g., spam or not spam), using algorithms like logistic regression and neural networks. 
  2. Regression models forecast continuous outcomes (e.g., house prices) using methods such as linear regression and decision trees.

Advantages and challenges of labeled data

Here we have the common advantages and challenges that labeled data can bring around:


  • High accuracy and specificity: Labeling helps train models to make precise predictions, essential for tasks like medical diagnosis and customer segmentation.
  • Structured learning framework: Provides clear, direct information for algorithms to learn from, reducing complexity and enhancing the learning efficiency.
  • Enhanced model reliability: With accurate labels, models can achieve higher reliability and performance in their predictions and classifications.


  • Time, effort, and cost: The manual process of labeling data is time-consuming, labor-intensive, and costly, making large-scale labeling projects challenging.
  • Quality and bias: Ensuring high-quality, unbiased labels is crucial; poor labeling can lead to inaccurate predictions and model biases. 
  • Scarcity of labeled data: For rare or very specific scenarios, acquiring sufficiently labeled data can be difficult, limiting model training and applicability.

What is unlabeled data

Unlabeled data is the information collected without any tags that explain or categorize the content. Unlike labeled data, which includes explicit labels indicating the nature or characteristics of the data, unlabeled datasets lacks these direct markers.

This datatype is raw and unprocessed, showing as a collection of features without any predefined meaning or classification. 

Unlabeled data is more abundant and readily available than labeled data because it does not require the intensive manual effort of labeling, making it a valuable resource for exploratory analysis and machine learning models that can learn from it without supervision. 

Common sources of unlabeled data

Unlabeled data can be sourced from a variety of platforms and mediums, reflecting the vast amount of information generated by digital activities. 

  • Social media platforms

Endless streams of text, images, and videos generated by users, offer insights into public opinion, trends, and behaviors.

  • Websites and blogs 

Rich textual and multimedia content, including articles, reviews, and comments, is useful for market research and sentiment analysis.

  • Sensors and IoT devices 

Real-time information from environmental sensors, smart home devices, and wearables, capturing everything from temperature readings to activity levels.

  • Customer databases 

Transaction records, browsing histories, and interaction logs from businesses, reveal patterns in consumer behavior and preferences.

  • Public records and datasets 

Government publications, census data, and open-source datasets, provide a broad spectrum of information for analysis.

  • Medical records

Anonymous patient health records are valuable for epidemiological studies and healthcare research.

  • Financial markets 

Stock prices, trading volumes, and financial indicators are critical for market analysis and algorithmic trading.

  • Satellite imagery 

Earth observation datasets are used in environmental monitoring, urban planning, and agriculture.

Applications and examples of unlabeled data

Unlabeled data finds application in numerous fields, particularly in tasks that require pattern recognition, market analysis, and customer segmentation. 

  • Market analysis: Analyzing browsing and purchase patterns from e-commerce platforms to understand consumer preferences and trends.
  • Cybersecurity: Using network traffic info to detect anomalies and potential security breaches without predefined threat signatures.
  • Customer segmentation: Clustering customers based on transaction data and interaction patterns to tailor marketing strategies.
  • Environmental monitoring: Tracking climate change and pollution levels through sensor data from various environmental monitoring devices.
  • Fraud detection Identifying unusual patterns in financial transactions that could indicate fraudulent activity, using anomaly detection models.
  • Content recommendation Utilizing user interaction activities on streaming platforms to recommend movies, music, or articles.
  • Astronomical research Classifying celestial objects and phenomena by analyzing huge datasets from telescopes and space missions.

Unsupervised learning models

Unsupervised learning models are designed to work with unlabeled data, identifying patterns, relationships, and structures within the data without any external guidance or labels. 

These models can discover hidden patterns or inherent structures that are not immediately apparent. 

Clustering algorithms, like K-means or hierarchical clustering, group data into clusters with similar features. 

Dimensionality reduction techniques, such as Principal Component Analysis (PCA), simplify data by reducing its features while preserving essential patterns. 

Anomaly detection models identify outliers that deviate significantly from the majority of the data, useful in fraud detection and network security. 

Unsupervised learning models are essential tools for deriving insights from unlabeled data, enabling exploratory data analysis, feature learning, and complex system understanding without predefined outcomes or classifications.

Advantages and challenges of unlabeled data

The advantages and challenges of using unlabeled data in machine learning and data analysis are a complex set that balances ease of access and potential insights against the complexities of processing and application.


Abundance and availability: Unlabeled data is far more abundant and accessible than labeled data, providing a wealth of information for analysis without the need for extensive labeling efforts.

Cost-effectiveness: Since unlabeled data does not require manual annotation by experts, it is generally cheaper and less labor-intensive to collect, making it an attractive option for large-scale projects.

Flexibility for exploration: The lack of predefined labels allows for open-ended exploration of the data, offering opportunities to uncover unexpected patterns, correlations, and insights that might not be evident with a labeled dataset.

Broad application potential: Unlabeled data can be utilized in a wide range of applications, including clustering, dimensionality reduction, and anomaly detection, across various fields such as marketing, cybersecurity, and environmental monitoring.


Lack of structure: The absence of labels means that the data lacks inherent structure, making it challenging to determine its relevance or to apply it directly to specific predictive or classification tasks.

Complex preprocessing: Unlabeled data often requires more sophisticated preprocessing and exploration efforts to identify useful patterns or structures, necessitating advanced analytical skills.

Dependency on advanced algorithms: The analysis of unlabeled ones typically relies on complex unsupervised learning algorithms, which can be more difficult to tune, interpret, and validate compared to supervised learning models.

Limited application scope: Direct applications of unlabeled data to tasks that require specific outcomes, such as classification or regression, are limited without undergoing an initial labeling process.

Quality assurance: Ensuring the quality, consistency, and relevance of unlabeled data can be challenging, as there is no straightforward metric or method to verify its accuracy or applicability to a given problem or analysis.

Advantages Challenges
Abundance and availabilityLack of structure
Cost-effectivenessComplex preprocessing
Flexibility for explorationDependency on advanced algorithms
Broad application potentialLimited application scope
Quality assurance

Labeled vs unlabeled data: key differences

Collection and preparationInvolves a significant effort in manually annotating data, making it time-consuming and costly.Easier and less expensive to collect as it requires no manual labeling, leading to greater abundance.
UsagePrimarily used in supervised learning models for specific tasks like classification and regression.Utilized in unsupervised learning for tasks like clustering, anomaly detection, and dimensionality reduction.
Analysis complexityStraightforward to use in model training as the labels provide clear guidance on what the model needs to learn.Requires complex algorithms and techniques to uncover patterns and insights due to the absence of guidance from labels.
CostMore expensive to prepare due to the need for accurate labeling by experts.Cheaper to acquire as it bypasses the labor-intensive labeling process.
Application scopeSuited for precise tasks where the desired outcome is known and defined.Offers flexibility for exploratory data analysis, allowing for the discovery of new insights without predefined outcomes.
Data quality and reliabilityQuality and reliability depend on the accuracy of the labeling process; errors or biases in labels can mislead the model.Quality assurance is challenging since there’s no direct way to validate the data against specific outcomes or labels.
AvailabilityLess abundant compared to unlabeled data due to the effort required in labeling.More readily available and accessible, as it can be collected from numerous sources without the need for labeling.

Decision factors for choosing labeled or unlabeled data

When deciding whether to use labeled or unlabeled data, consider these key factors: 

  • Project objectives: Choose labeling for precise predictions or classifications. Unlabeled data is better for exploring hidden patterns.
  • Resources: Labeling requires more resources for annotation. Unlabeled data is cost-effective but may need advanced analysis techniques.
  • Expertise: If you have access to sophisticated processing and unsupervised learning algorithms, unlabeled data can be a viable option.
  • Desired outcomes: For tasks with well-defined outcomes, labeling is essential. For exploratory analysis without predefined outcomes, unlabeled data is suitable.

Techniques for working with unlabeled data

Unlabeled data presents both opportunities and challenges in data analysis and machine learning. Here are some key techniques for converting unlabeled data into a form that’s ready for analysis or model training. 

Manual labeling

Manual labeling entails human annotators reviewing and assigning labels to data points based on their judgment. This approach is highly accurate when performed by experts familiar with the task context, making it ideal for complex or nuanced datasets where precision is critical. However, manual labeling is time-consuming and can be expensive for large datasets, limiting its scalability.


Crowdsourcing involves the crowd to label data, typically through platforms that allow multiple individuals to contribute. This method can significantly speed up the labeling process and reduce costs compared to expert annotation. Crowdsourcing is particularly effective for tasks that require basic knowledge or common sense, though it may introduce variability in data quality due to the diverse backgrounds of the contributors.

Automated labeling with machine learning

Automated labeling employs machine learning models to annotate unlabeled data. This can be achieved through techniques like semi-supervised learning, where a small set of labeled data is used to train a model that then labels the rest of the dataset, or through transfer learning, where a model trained on a different but related task is adapted to label the new dataset.

Automated methods can process large volumes of data quickly and at a lower cost than manual methods, though they may require initial labeled data to train the model and can sometimes produce less accurate results.

Each of these techniques offers a different mix of accuracy, cost, and scalability, making them suitable for different situations depending on the specific needs of the project and the nature of the data involved.

Transitioning from unlabeled to labeled: processes and tools

Transitioning from unlabeled to labeled data is a crucial step in preparing for machine learning projects and enhancing data analysis. This process involves converting raw data into a structured format that machine learning algorithms can understand and learn from. Here’s an overview of the processes and tools involved in this transition:


  1. Data assessment: The first step involves evaluating the unlabeled data to understand its nature, quality, and potential biases. This helps in determining the most suitable labeling approach.
  1. Defining labeling criteria: Establish clear, consistent criteria for labeling to ensure that the data is categorized accurately and uniformly. This includes defining categories, tags, or classes that are relevant to the machine-learning task.
  1. Selection of labeling technique: Depending on the dataset size, complexity, and the project’s budget and timeline, choose between manual labeling, crowdsourcing, and automated labeling techniques.
  1. Quality control: Implement quality control measures to ensure the reliability of the labeled data. This could involve reviewing a sample of the labeled data for accuracy or using consensus mechanisms in crowdsourcing.
  1. Iterative refinement: Labeling is often an iterative process, where initial rounds of labeling are reviewed and refined to improve accuracy and consistency across the dataset.


Crowdsourcing platforms:

Automated labeling tools

The choice of processes and tools depends on the specific requirements of the project, including the desired level of accuracy, available resources, and the volume of data. Balancing these factors effectively can greatly enhance the value of the labeled data for subsequent machine-learning efforts.