The Differences Between Data Labelling And Classification

Infosearch is an expert provider of data labelling services and classification services along with various data annotation services. Even though both data labeling and classification are concepts that are often introduced together with machine learning and data science, they are different. This post shows these differences are essential to understand when it comes to designing or deploying machine learning models.

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1. Definition

• Data Labeling:

The process of manually or automatically assigning labels or tags to raw data to create a structured dataset. This step is typically part of data preparation and is used to generate training datasets for machine learning models.

• Classification:

A machine learning task in which a trained model predicts the category or class of a given data point based on labeled training data. Classification happens during the inference or evaluation stage.

2. Role in Machine Learning

• Data Labeling:

o Occurs during the data preparation phase.

o Provides the labeled data required to train supervised machine learning models.

o Involves human annotators or semi-automated tools for accuracy.

• Classification:

o A machine learning algorithm’s task during the training or prediction phase.

o Models use labeled data to learn patterns and assign labels to new, unseen data.

3. Examples

• Data Labeling:

o Tagging an image of a dog with the label “dog.”

o Assigning sentiment labels like “positive,” “negative,” or “neutral” to customer reviews.

o Labeling medical images as “tumor” or “no tumor.”

• Classification:

o Predicting whether an email is “spam” or “not spam” based on a trained model.

o Determining if an image contains a “cat,” “dog,” or “bird” using a convolutional neural network.

o Categorizing customer queries into “billing,” “technical support,” or “general inquiry.”

4. Techniques

• Data Labeling:

o Performed manually or with tools like Labelbox, Appen, or Amazon SageMaker.

o May involve crowdsourcing platforms for large-scale labeling.

o Requires human expertise for complex or ambiguous data.

• Classification:

o Implemented using machine learning algorithms such as decision trees, support vector machines (SVMs), or neural networks.

o Techniques include supervised learning, feature extraction, and hyper parameter optimization.

5. Scope

• Data Labeling:

o Limited to preparing data for machine learning tasks.

o Does not involve prediction or model learning.

• Classification:

o Encompasses the entire process of model training and deployment for prediction.

o Is an outcome of using labeled data created during the labeling process.

6. Automation and Human Interaction

• Data Labeling:

o It may also take relatively a lot of human intervention especially when processing large or unformatted data.

o Semi-automated tools are available for use as a support tool, but human validation is often used to verify the data.

• Classification:

o Performed entirely by machine learning models after training.

o Automation is key, with minimal to no human involvement during prediction.

7. Key Challenges

• Data Labeling:

o Time-consuming and labor-intensive, especially for large datasets.

o Ensuring consistency and accuracy in labeling.

• Classification:

o Model overfitting or under fitting.

o Handling imbalanced datasets or ambiguous classes.

Conclusion

• Data Labeling is a foundational process in supervised learning, ensuring that machine learning models have the structured data they need to learn effectively.

• Classification is the application of those labeled datasets to build and deploy models capable of making accurate predictions on new data.

Taken together, these processes constitute a key cycle in the development of intelligent, data-forging systems.

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