Labeled and Unlabeled Data- What is the difference??
Do you want to know how to label data, continue reading the article below!
What is Data?
Data refers to any relevant information that can be processed into a form to make an analysis. Data comes in different shapes and sizes. In today`s day of technology, data exists in a multitude of forms- Think text, images, pdf, videos, excel sheets, drive — Yes, you read it right. All of this is all data, data, and data.
However, not all data are of the same kind. There is a further classification of data, which is mentioned below
# 1 Labeled Data
# 2 Unlabeled Data
What is unlabeled data?
Any data that does not have any labels specifying its characteristics, identity, classification, or properties can be considered unlabeled data. For example photos, videos, or text that do not have any category or classification assigned to it can be referred to as unlabeled data.
Now that we have a fair understanding of unlabeled data, let’s look into what is labeled data?
Labeled Data?
Any data which has a characteristic, category, or attributes assigned to it can be referred to as labeled data. For example, a photo of a cat, the height of a human, price of a product is some examples of labeled data.
What is data labeling?
Data labeling is defined as a process of identifying raw data- like text, pdf, files, images and classifying and adding one or more labels to it to enable machine learning models to learn from it.
Labeling helps the machine learning model identify the attributes of the data to analyze and make predictions. Over time, machine learning starts identifying the data and can make accurate predictions seamlessly.
Machine Learning can be classified into 3 categories:
#1 Supervised Learning
# Unsupervised Learning
# Reinforcement Learning
In the case of unsupervised learning, Data Scientists feed unlabelled data into the Machine Learning models to help the model learn from each data point and identify the characteristics
Let's assume that we have images of animals- Say Cats, Dogs, and Foxes. We are now developing a Machine Learning algorithm to help differentiate between the three different animals.
If we feed the model with labeled data, it will easily identify and classify the images of the cat, dog, and fox and classify them.
In case the images do not have any label attached, the machine learning model will have to identify each image and understand the peculiar characteristics- Color. body shape, facial features, and other details to learn and classify them into different categories.
Here is a chart to help you understand the differences between labeled and unlabeled data.
How to efficiently apply data labeling?
The key to building successful and efficient Machine Learning models is to continuously feed them with a massive amount of high-quality data. Over time, the model will get better and better at making accurate predictions.
To begin with, Data scientists need to train the model that is labeled by humans. The model will start applying labels automatically to all the data it understands and will pass back the rest of the data, that it does not understand back to humans for annotation.
The returned data is once again fed into the model to retrain and improve its capability to automatically assign labels to new data. Over time, the machine will become proficient in labeling most of the data on its own without requiring much supervision.
Below is the flowchart diagram depicting the flow for efficient labeling
Ways of labeling data
There are different ways to achieve the labeling process. It all depends on the organizational capabilities and resources at its disposal.
# 1 Internal Sourcing:
Large companies who have a dedicated in-house Data science team can engage internal resources to label raw data.
# 2 Script Labelling:
Data scientists can write code/scripts and run them to automatically annotate data. This reduces human intervention to a certain extent, but not completely.
# 3 Agencies:
In case the organization does not have sufficient bandwidth, it can outsource data labeling to agencies that specialize in such tasks.
Advantages and Disadvantages of Data labeling
Although data labeling is a time-consuming and tedious task, it is nonetheless worth the effort and time. Labeled data helps in making accurate predictions which can help organizations to increase sales and profits.
Advantages
#1 Accurate Predictions:
Machine Learning models are as good as the data provided.No matter how good the model is, if fed with useless or irrelevant data, it will fail to make accurate predictions.
Accurate data helps the machine learning model to train in an optimal way and make better and more useful predictions
Disadvantages
# 1 Expensive:
Although data labeling is vital for a successful machine learning model, it consumes a lot of time and is also expensive to perform. The whole process of setting the data pipeline and setting the process does take a significant amount of resources.
Not all organizations have the means and methods to assign significant resources.
#2 Errors:
Humans do tend to make errors while labeling data. That, of course, is normal. Labeling data incorrectly can lead to wrong predictions.
In order to resolve the issue, Quality Assurance resources must validate all the labeling at the end to ensure there is no discrepancy.
Types of Data Labeling
#1 NLP( Natural Language Processing):
NLP is a subset of AI that leverages, Machine Learning and Deep Learning to train the model by identifying and tagging texts for sentiment analysis. Entity name recognition and optical character recognition.
#2 Audio Processing:
Audio processing involves formatting all kinds of speeches and sounds- like chirping, barking, hissing, glass breaking, door banging, etc to use it for machine learning models.
I hope this article gives you a clear picture of labeled and unlabeled data. Do let me know in the comment section your thoughts on the same?
