Synthetic Data vs Tagged Data,
which one to choose?
January 3, 2023
There is a debate raging in the world of data right now: synthetic data vs tagged data. Which one should you choose for your business? The answer, of course, depends on your needs.
Choose the right Data
When it comes to AI data, there are two main types: synthetic data and tagged data. So, which one should you choose?
Synthetic data is artificially generated data that can be used to train machine learning models. This data is typically generated by algorithms, rather than humans. The advantage of synthetic data is that it can be generated in large quantities and can be tailored to the specific needs of the machine learning model. However, synthetic data may not always be realistic, which can limit its effectiveness.
Organizations are now turning to artificial intelligence (AI) and machine learning (ML) to help make their data work harder. But what happens when the data they train their models on is inaccurate or incomplete?
Tagged Data high performance & accuracy
There are many factors to consider when choosing between synthetic data and tagged data:
1- Performance & accurracy
Tagged data is often more accurate than synthetic data because it is collected from real-world sources. This means that it is less likely to contain errors. In addition, tagged data can be more easily verified for accuracy.
2- Speed VS Accurracy
Synthetic data, on the other hand, is generated by algorithms and may not be as accurate as tagged data. However, synthetic data can be generated much faster than tagged data, so it may be preferable in some situations where speed is more important than accuracy.
3- Train & Test
Tagged data is often used to train and test AI models. This is because tagged data can be easily divided into a training set and a test set. Tagged data can also be used to create synthetic data sets. Synthetic data sets are created by using a generative model to generate new data points that are similar to the training data set.
4- Tagged Data Accurracy
Tagged data is often more accurate than synthetic data sets. This is because the tagging process ensures that only relevant information is included in the training data set. Tagged data can also be more easily divided into a training set and a test set.
However, tagged data can be more expensive to obtain than synthetic data sets. In addition, the accuracy of tagged data can vary depending on the quality of the tagging process.
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