irrelevant dataset

USAGE SUMMARY

The phrase "irrelevant dataset" is correct and usable in written English.
It can be used when referring to a dataset that does not have any significance or connection to the topic or analysis at hand. Example: "In our analysis, we found that the irrelevant dataset did not contribute any valuable insights to our research findings."

✓ Grammatically correct

Science

Formal & Business

News & Media

Human-verified examples from authoritative sources

Exact Expressions

1 human-written examples

The work described below was motivated by the need for a tool to address the following two peculiarities of RNA-based metagenomic/metatranscriptomic data in the context of viral genome assembly: (1) highly uneven coverage across an entity that (2) comprises only a tiny portion of a massive, complex, largely irrelevant dataset.

G3: Genes, Genomes, Genetics

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

To cope with the challenges of big data in e-commerce, special data relationship discovery techniques are to be applied to reveal seemingly irrelevant datasets or data attributes to extend the information about specific customers for the credit scoring purpose.

Financial Innovation

The union of both searching results was taken, followed by manual filtration to exclude irrelevant datasets that, for example, came from cell lines or specific cell types.

BMC Genomics

It is quite clear that the chosen method is irrelevant on datasets made of sufficiently similar sequences (>50% pair-wise identity).

Bioinformatics

Species of the Flavi section were grouped well together, with high bootstrap support (>80%), irrelevant to the dataset used.

BioMed Research International

This assumption of consistent coverage is irrelevant for metagenomic datasets, where the level of coverage for each genome will be different and dictated by the number of cells and genome copies of each organism in the locally sampled ecosystem.

G3: Genes, Genomes, Genetics

More precisely, sampling can be regarded as reducing the "amount of data" entered into a data analyzing process while dimension reduction can be regarded as "downsizing the whole dataset" because irrelevant dimensions will be discarded before the data analyzing process is carried out.

Journal of Big Data

Users may examine the alignment of the sequences and the output tree topology, attempt to identify irrelevant sequences from the dataset and, then, resubmit the job.

Plosone

Since the vast majority of the genes in a given dataset are irrelevant to the survivals of the studied patients, the result is that many of the inputs to the predictive model are superfluous and thus reduce the accuracy of the model for prediction.

BMC Genomics

However, some genes that seemed irrelevant or redundant in one dataset may have been crucial for HT identification in the other datasets.

BMC Genomics

However, three major problems usually appear using the ELM structure: (i) the dataset may have irrelevant variables, (ii) choosing the number of neurons in the hidden layer would be difficult, and (iii) it may encounter the singularity problem.

Measurement

Expert writing Tips

Best practice

When discussing data analysis or machine learning, clearly identify and exclude "irrelevant datasets" early in the process to improve model accuracy and efficiency. Be specific about why the dataset is deemed irrelevant.

Common error

Avoid treating all datasets as equally valuable. Failing to identify and filter out "irrelevant datasets" can lead to skewed results, wasted resources, and inaccurate conclusions. Always assess the relevance of each dataset before including it in your analysis.

Linguistic Context

The phrase "irrelevant dataset" functions as a noun phrase where the adjective "irrelevant" modifies the noun "dataset". It serves to categorize and describe a particular type of data as being not pertinent or applicable to a specific context or analysis, as confirmed by Ludwig.

Expression frequency: Rare

Frequent in

Science

50%

News & Media

25%

Formal & Business

25%

Less common in

Wiki

0%

Encyclopedias

0%

Reference

0%

Ludwig's WRAP-UP

In summary, the phrase "irrelevant dataset" is a noun phrase used to describe data that is not pertinent or applicable to a specific analysis. Ludwig AI confirms its grammatical correctness and usability. While its frequency is relatively rare, it finds primary usage in scientific, formal and business contexts. To enhance clarity in writing, consider alternatives like "unrelated data" or "inapplicable data". Remember to assess data relevance to avoid skewed results in your analyses.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Unrelated data

Focuses on the lack of connection or relation to the subject matter.

Inapplicable data

Emphasizes that the data cannot be properly applied or used in the given situation.

Immaterial data

Highlights the lack of significance or importance of the data.

Unnecessary data

Stresses that the data is superfluous and not required for the analysis.

Non-essential data

Similar to 'unnecessary data', but focuses on the data not being crucial.

Extraneous data

Indicates that the data is external and not part of the core information needed.

Peripheral data

Suggests the data is on the edge of relevance and not directly connected.

Inconsequential data

Highlights that the data has no significant effect or result.

Marginal data

Similar to 'peripheral data', focusing on the limited importance or relevance.

Unserviceable data

Emphasizes the impracticality of using the data for a specific purpose.

FAQs

How can I identify an "irrelevant dataset"?

An "irrelevant dataset" is one that does not contribute meaningfully to the analysis or problem you're trying to solve. Assess the dataset's variables, context, and relationship to your research question. If it doesn't offer valuable insights, it's likely irrelevant.

What are some alternatives to saying "irrelevant dataset"?

You can use alternatives like "unrelated data", "inapplicable data", or "immaterial data" depending on the specific nuance you want to convey.

How does including an "irrelevant dataset" affect data analysis?

Including an "irrelevant dataset" can introduce noise, skew results, and reduce the accuracy of your analysis. It's crucial to filter out irrelevant data to ensure the validity of your findings.

In what contexts is the term "irrelevant dataset" most commonly used?

The term "irrelevant dataset" is frequently used in scientific research, data science, and machine learning to describe data that does not contribute to the study or model's objectives.