synthetic data

USAGE SUMMARY

"synthetic data" is a correct and usable phrase in written English.
It refers to data that is artificial, having been generated by a computer or other artificial means. For example, "We use synthetic data to test our machine-learning algorithms."

✓ Grammatically correct

Science

News & Media

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

Merging consumer and voter behavior data, Obama's team then modeled and clustered the information, creating synthetic data that enabled them to make incredibly granular calculations about available voters.

The Guardian

Figure 4 Synthetic data.

EURASIP Journal on Advances in Signal Processing

Fig. 4 Synthetic data simulation results.

EURASIP Journal on Advances in Signal Processing

Figure 17 Robots performance with synthetic data.

EURASIP Journal on Audio, Speech, and Music Processing

We generate two synthetic data sets.

EURASIP Journal on Advances in Signal Processing

Fig. 1 Reconstruction for exact synthetic data.

Pacific Journal of Mathematics for Industry

Like we noted above, when generating a differentially private synthetic data set, we can generate either a partially synthetic data set or a fully synthetic data set.

Data Science and Engineering

Synthetic data were used to analyze the problem.

Construction and Building Materials

Data from three wells and synthetic data are used.

Journal of Petroleum Science and Engineering

Note that toroidal fields where omitted from the synthetic data.

Earth, Planets and Space

Then the sample eigenvalues of synthetic data are calculated.

EURASIP Journal on Advances in Signal Processing

Expert writing Tips

Best practice

When using "synthetic data" in technical writing, clearly define the parameters and methods used to generate the data to ensure reproducibility and transparency.

Common error

Don't assume that results obtained using "synthetic data" directly translate to real-world scenarios without proper validation. Always acknowledge the limitations and potential biases introduced by the artificial nature of the data.

Linguistic Context

The phrase "synthetic data" primarily functions as a noun phrase, where 'synthetic' modifies 'data'. According to Ludwig AI, this phrase is grammatically correct and widely used. Its main role is to describe data that is artificially created rather than naturally occurring.

Expression frequency: Very common

Frequent in

Science

66%

News & Media

22%

Formal & Business

6%

Less common in

Encyclopedias

0%

Wiki

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "synthetic data" is a grammatically correct and very common noun phrase used to describe artificially generated data. As Ludwig AI confirms, it's frequently employed in scientific and technological contexts, serving to inform about the non-natural origin of the data. The phrase maintains a formal register, appearing predominantly in academic and scientific publications. When using "synthetic data", it's important to define generation methods and acknowledge its limitations compared to real-world data. Related terms include "artificial data" and "simulated data". Understanding its usage helps ensure clarity and accuracy in technical writing.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

artificial data

Replaces "synthetic" with a direct synonym, emphasizing the non-natural origin of the data.

simulated data

Focuses on the method of creation, highlighting the simulation process used to generate the data.

computer-generated data

Specifies the tool used for data creation, emphasizing the role of computers.

artificially generated data

Combines "artificial" and "generated" for a more descriptive alternative.

notional data

Highlights the hypothetical or theoretical nature of the data.

fabricated data

Implies a constructed or manufactured origin of the data, though it can sometimes carry a negative connotation of being intentionally misleading.

modeled data

Emphasizes that the data is a product of a model, reflecting specific assumptions and structures.

virtual data

Suggests the data exists in a virtual or digital environment, created for specific purposes.

proxy data

Indicates the data is used as a stand-in for real-world data that is unavailable or difficult to obtain.

surrogate data

Similar to proxy data, implying the data is a substitute used when real data is lacking.

FAQs

How is "synthetic data" typically used in machine learning?

"Synthetic data" is often used to train machine learning models when real-world data is scarce, expensive, or poses privacy concerns. It can help improve model performance and generalization capabilities.

What are the advantages of using "synthetic data" over real data?

"Synthetic data" offers advantages such as cost-effectiveness, scalability, and control over data characteristics. It also avoids privacy issues associated with real data and can be tailored to address specific research questions or training needs.

What are some limitations of using "synthetic data"?

Limitations include the potential for bias if the synthetic data doesn't accurately reflect real-world distributions, and the risk of overfitting to the synthetic data, which may reduce performance on real data. Careful validation is crucial.

What are some alternatives to using "synthetic data"?

Alternatives include using data augmentation techniques on real data, employing transfer learning from pre-trained models, or exploring federated learning to train models on decentralized real-world datasets while preserving privacy. You can also consider using "simulated data" or "artificial data".