irrelevant datasets

USAGE SUMMARY

The phrase "irrelevant datasets" is correct and usable in written English.
It can be used when referring to datasets that do not have any significance or importance to the context or analysis at hand. Example: "In our analysis, we found that the irrelevant datasets only added noise to our results and did not contribute to our findings."

✓ Grammatically correct

Science

Financial Innovation

BMC Genomics

G3: Genes, Genomes, Genetics

Bioinformatics

BioMed Research International

Journal of Big Data

Plosone

BMC Genomics

Environmental Sciences Europe

BMC Medical Research Methodology

BMC Genomics

Measurement

BMC Medical Research Methodology

BMC Genomics

EURASIP Journal on Advances in Signal Processing

Plosone

BMC Systems Biology

Bioinformatics

BMC Medical Research Methodology

G3: Genes, Genomes, Genetics

EURASIP Journal on Audio, Speech, and Music Processing

Drug Safety

Complex & Intelligent Systems

Plosone

BMC Systems Biology

BMC Medical Research Methodology

BMC Medical Research Methodology

BMC Medical Genomics

Bioinformatics

TechCrunch

Journal of Natural Gas Science and Engineering

Human-centric Computing and Information Sciences

BMC Medical Genomics

BioMed Research International

Vice

The New York Times

The Guardian - Books

The Guardian

The Economist

The Economist

The Guardian - Sport

The New York Times - Books

The New York Times - Sports

The New Yorker

The New York Times - Magazine

The New York Times

The New York Times

Independent

The New York Times - Arts

The Economist

The Economist

The Guardian

The New York Times - Sports

The New York Times

The New Yorker

The Economist

Independent

The Economist

Human-verified examples from authoritative sources

Exact Expressions

2 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

Human-verified similar examples from authoritative sources

Similar Expressions

58 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

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

Bioinformatics

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

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

BioMed Research International

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

Thus, from a methodological point of view and based on our datasets, it was irrelevant whether the geometries of arable fields precisely represent the real cultivation situation of a specific crop or whether the geometries are taken from topographic databases such as ATKIS with coarser geometries.

Environmental Sciences Europe

The logic is that under H0 gender is irrelevant, and so the permuted datasets generate the reference distribution for the test statistic.

BMC Medical Research Methodology

Expert writing Tips

Best practice

When analyzing data, prioritize identifying and excluding "irrelevant datasets" early in the process to prevent skewed results and wasted resources. This ensures your analysis focuses on the most pertinent information.

Common error

A common mistake is failing to filter out "irrelevant datasets", which can introduce noise and bias into your analysis. Always critically evaluate the relevance of each dataset before including it in your research or models.

Linguistic Context

The phrase "irrelevant datasets" functions as a noun phrase modified by an adjective. It describes datasets that are not pertinent or applicable to a specific context or analysis. As Ludwig AI states, the phrase is grammatically correct and usable in written English.

Expression frequency: Rare

Frequent in

Science

80%

News & Media

10%

Formal & Business

10%

Less common in

Wiki

0%

Encyclopedias

0%

Reference

0%

Ludwig's WRAP-UP

In summary, "irrelevant datasets" refers to data collections that do not contribute meaningfully to a given analysis or research question. According to Ludwig AI, the phrase is grammatically correct. It is most commonly used in scientific contexts to identify and exclude data that could skew results or waste resources. While not highly frequent, understanding and identifying "irrelevant datasets" is essential for ensuring data integrity and improving the accuracy of models and analyses. Consider using alternatives like "unrelated datasets" or "extraneous datasets" depending on the specific nuance you wish to convey.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

datasets of no relevance

Rephrases the original meaning by emphasizing the absence of relevance.

unrelated datasets

Focuses on the lack of connection or pertinence to the topic at hand.

datasets lacking pertinence

Highlights the absence of a direct relationship to the issue being considered.

extraneous datasets

Highlights that the datasets are not essential or integral to the analysis.

immaterial datasets

Emphasizes the lack of significance or consequence of the datasets.

inapplicable datasets

Indicates that the datasets are not appropriate or suitable for the given purpose.

unnecessary datasets

Stresses that the datasets are not required or needed in the analysis.

redundant datasets

Implies the datasets are repetitive and add no new information.

inconsequential datasets

Highlights that the datasets have little to no impact on the outcomes.

insignificant datasets

Focuses on the minor importance or value of the datasets.

FAQs

How can I identify "irrelevant datasets" in my analysis?

To identify "irrelevant datasets", assess whether the data contributes meaningfully to your research question or analysis goals. Datasets that do not provide unique insights or address the core objectives can be considered "unrelated datasets" and potentially irrelevant.

What are the consequences of including "irrelevant datasets" in my research?

Including "irrelevant datasets" can lead to skewed results, increased complexity, and wasted resources. It can also obscure the true relationships within the relevant data, making it harder to draw accurate conclusions. Therefore, carefully filtering "extraneous datasets" is crucial for accurate analysis.

Are there tools or techniques to help remove "irrelevant datasets"?

Yes, there are various techniques, including feature selection algorithms, dimensionality reduction, and data cleaning processes. These methods help identify and remove "irrelevant datasets" by assessing their statistical significance, relevance to the research question, or impact on the overall model performance. Proper implementation of these techniques ensures that only pertinent data is retained.

What's the difference between "irrelevant datasets" and "outliers"?

"Irrelevant datasets" are datasets that, as a whole, do not contribute to the analysis's objectives, while outliers are individual data points within a dataset that deviate significantly from the norm. Removing "inapplicable datasets" involves excluding entire data collections, while outlier handling focuses on individual data points within a relevant dataset. Both are important for data quality, but address different issues.