What is Data Annotation?
Data annotation in machine learning is the process of labeling the data, accompanied with notes on how it should be used. It is often done so that there can be an understanding of what information this particular data has related to the problem.
Types of Data Annotation
There are 2 main types of data annotation:
- Descriptive data annotation is used to define what descriptive information a particular attribute has related to the problem. For example, if you're using a self-driving car, you can use it to describe different rules and parameters that need to be examined by the car. Descriptive data annotation is similar to labeling a set of information.
- Data conditioning data annotation is used for training or evaluating algorithms. It is an important type of data annotation because it is used to predict how the product will perform in its environment.
Why Data Annotation is so important in Machine Learning?
There are a number of reasons as to why data annotation is important. Some of these include:
- It allows the machine to understand the context of a piece of data
Without a significant amount of data annotation, your program may fail. This is because your program will not have an understanding as to what each piece of data is actually describing. In order for your program to properly execute, it must know what each piece of data refers to and how it should be used.
2. Data annotation for machine learning makes it easier to train an algorithm
If the data of the program is not annotated, then the program will be less likely to be able to learn new values. This will make it more difficult for the machine of the program to acquire new things, develop new concepts or pick up on trends. This is because of how much effort would need to be put into programming something that has very little variation in it.
3. Data annotation helps create a more complete model
You will have more information to work with through data annotation. This is because when you are programming for machine learning, the type of data that is being used plays a large role in what is being programmed.
What’s recommended in machine learning?
Many communities have suggested that if you are going to program for machine learning, then it would be extremely helpful to have extensive data annotation done on the raw data that has been collected. This is the best method of programming for machine learning because it takes advantage of the fact that coding for machine learning needs to be done with the use of data. It also gives you an organized form of data that can be used to create your program’s code.
When should you use data annotation?
You should use data annotation during the development phase of any machine learning project. For example, you can use it when creating algorithms and searching for optimum parameters and hyperparameters. It can be used to find out how accurate your algorithm is, and then compare it with other algorithms that could perform better on the same problem.
You can use data annotation to improve the quality of products. For example, you can use it to make sure that the product you're creating is correct and prevents mistakes or errors that could cause problems during its release. Additionally, researchers can use your data annotation to study your product's performance and create new algorithms that are better at solving the problem for which they are trying to find a solution.
As stated earlier, data annotation will help you analyze how accurate your product is at solving a particular problem. They will help you judge how the product performs in its environment.
Conclusion
Many models of predictive machine learning are more accurate when they are trained on annotated data. Data annotation forces the model to learn what information is "important" by marking it with weights or labels that indicate this importance. This improves the accuracy of the model, but comes at a cost: annotators usually do not have time to spend hours manually training their own dataset annotations.
This means that in order to get accurate predictions on new data, you need thorough and careful data annotation which is time-consuming and expensive. Do not worry, here at Kotwel, we can help you obtain high quality, accurate data for your machine learning projects.
Kotwel is an emerging data service provider in Vietnam, offering high-quality AI training data for machine learning and AI. It provides data services that help get more out of your algorithms by generating, labeling and validating unique and high-quality training data, specifically tailored to your needs.