What is data labeling?
Data labeling or also known as data classification is the process of labeling unlabeled data that needs to be used for machine learning. In simple terms, data labeling refers to targeting or annotating data by labeling the key features of those objects. It is a process that can be performed manually but is mostly performed by making use of certain labeling tools.
Machine learning algorithms learn from labeled data, however, in order to do so, they need to recognize repetitive patterns in label data. Consequently, after a sufficient amount of labeled data is processed, machine learning algorithms can learn from data that has not been labeled.
Importance of data labeling
Machine learning is widely being adopted by businesses to automate decision making and benefit from new business opportunities. In order to perform efficient machine learning, it is vital that data labeling is performed splendidly.
According to an article by McKinsey, data labeling is considered as the most challenging limitation in adopting machine learning for businesses.
Increasing data labeling efficiency
Supervised learning requires the most amount of labeled data. Through this method, it can learn from the labeled data and then use it to learn new or unlabeled data. Data labeling efficiency can be achieved by using the active learning approach.
Active learning is a semi-supervised approach that uses few unlabeled data in the initial stages, and as the learning progresses it makes use of the labeled data to perform learning and thus improve data labeling efficiency.
Types of data labeling software
Data labeling tools can be divided into different categories. Some tools can perform single segments such as image, text, or audio. Annotation tools can also differ from each other in annotating methods.
Some of the data labeling software are:
- Amazon Sagemaker Ground Truth
- Figure Eight
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