Data annotation is a critical part of training machine learning models. Businesses must provide their algorithms with high-quality training data to get the most accurate results. So what are data annotation’s main uses cases?
One of the most common uses for data annotation is label generation. This is when businesses use data annotation to add labels to their data. The tags can be anything from the name of an object to the sentiment of a review.
Basically, features are characteristics of the data that machine learning algorithms can use to make predictions. For example, if you’re trying to predict the price of a car, some features you might use are the car’s make, model, and year.
Label Quality Improvement
Another common use case for data annotation is label quality improvement. This is when businesses use data annotation to improve the quality of their labels. For example, if you have a dataset of customer reviews, you might use data annotation to improve the accuracy of your sentiment labels.
Model Performance Validation
Businesses also use data annotation to validate the performance of their machine learning models. You do so by annotating a dataset and then using it to test the accuracy of your model.
Unsupervised Conversion Into Supervised
Data annotation can also convert an unsupervised learning problem into a supervised learning problem.
Note: Data annotation is an integral part of machine learning, but it’s not the only thing you need to do. You also need to have high-quality data!