numerical attributes

USAGE SUMMARY

The phrase "numerical attributes" is correct and usable in written English.
It can be used in contexts such as data analysis, statistics, or when discussing characteristics that can be quantified. Example: "In our dataset, we need to focus on the numerical attributes to perform a regression analysis."

✓ Grammatically correct

Science

Human-centric Computing and Information Sciences

Data Science and Engineering

Brain Informatics

Information Sciences

EURASIP Journal on Wireless Communications and Networking

Journal of the Brazilian Computer Society

Knowledge-Based Systems

Wikipedia

Plosone

BMC Medical Informatics and Decision Making

BMC Systems Biology

Bioinformatics

BMC Medical Genomics

EURASIP Journal on Audio, Speech, and Music Processing

Lingua Sinica

Science Magazine

Journal of Cloud Computing

International Journal of Heat and Mass Transfer

BMC Genomics

Journal of Big Data

Neurocomputing

Journal of Systems and Software

Computers & Security

Human-verified examples from authoritative sources

Exact Expressions

31 human-written examples

Because real datasets are always a combination of numeric and nominal vales, for an algorithm that only takes nominal data, numerical attributes need to be discretized into nominal attributes before the learning algorithm.

Human-centric Computing and Information Sciences

The number of attributes is equals to 24 numerical attributes.

Vietnam Journal of Computer Science

The numerical attributes represent the blocks more accurately and flexibly with varying k.

Data Science and Engineering

Thus, selecting numerical attributes as blocking key can improve the scalability and be applied in many real-world scenarios.

Data Science and Engineering

We start from the original set of N examples described by nominal and numerical attributes that may contain unknown values.

Brain Informatics

For simplicity Manhattan distance is chosen as distance function in Eq. 4 and (mathcal {A_{QID}}) with only numerical attributes.

Human-centric Computing and Information Sciences

Human-verified similar examples from authoritative sources

Similar Expressions

29 human-written examples

A numerical attribute is generated for each word stem that is not in the list of stopwords and occurs at least ten times in one class.

EURASIP Journal on Audio, Speech, and Music Processing

The methods based on the first criterion can extract facts with no dimensions, and those based only on the cardinality may extract a fact with no numerical attribute (assuming the count as a default measure).

Vietnam Journal of Computer Science

Dynamic Generating k-anonymous Blocks We suppose (A_N) (numerical attribute) is selected to be the blocking key; then, we form blocks on the databases of (P_{1}), (P_{2}),..., and (P_{p}) ((pge 2)), respectively.

Data Science and Engineering

L is a finite set of levels, each level l (in ) L defined on a categorical domain Dom(l); H = (h_{1}),..., (h_{n}) is a restricted set of hierarchies, each characterized by: (i) a subset (L_{i}) (subseteq ) L of levels (all (L_{i}))'s are disjoint; (ii) a rollup total order > (L_{i}) of level ((L_{i})); M is a limited set of measures, each defined on a numerical attribute.

Vietnam Journal of Computer Science

Mean ± standard deviation is given for each numerical attribute.

BMC Medical Informatics and Decision Making

Expert writing Tips

Best practice

When working with machine learning algorithms, ensure that your "numerical attributes" are properly scaled or normalized to prevent features with larger values from dominating the learning process.

Common error

Avoid treating categorical data as "numerical attributes". If a feature represents categories (e.g., colors), use appropriate encoding techniques like one-hot encoding instead of directly inputting the category labels as numbers.

Linguistic Context

The phrase "numerical attributes" functions as a noun phrase that identifies quantifiable features within a dataset. Ludwig AI confirms its correctness and usability. It is frequently encountered in contexts involving data analysis and statistical modeling.

Expression frequency: Common

Frequent in

Science

70%

Human-centric Computing and Information Sciences

10%

Data Science and Engineering

10%

Less common in

Wikipedia

2%

Neurocomputing

1%

Lingua Sinica

1%

Ludwig's WRAP-UP

In summary, "numerical attributes" is a standard phrase in data analysis and related fields to describe quantifiable data features. Ludwig AI confirms that is correct and usable. It's primarily used in formal, scientific contexts, as seen in academic and research publications. When using this phrase, ensure that you are correctly distinguishing numerical data from categorical data and applying appropriate preprocessing and analytical techniques. Alternatives such as "quantitative characteristics" can be used depending on the specific context, but the core meaning remains consistent.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

numerical characteristics

Similar to "numerical attributes" but uses 'characteristics' instead of 'attributes'.

quantitative characteristics

Focuses on the quantifiable nature of the characteristics.

numeric features

Emphasizes the feature aspect and its numerical type.

measurable properties

Highlights the ability to measure these properties.

quantitative variables

Emphasizes the variable nature and quantifiability.

numerical data fields

Specifies that these are data fields of a numerical nature.

quantifiable metrics

Highlights the metric aspect and its quantifiability.

numerical parameters

Emphasizes that these are parameters and their numerical type.

quantitative indicators

Focuses on the indicator aspect and its quantifiability.

numeric indicators

Highlights the indicator role using the term 'numeric'.

FAQs

How are "numerical attributes" used in data analysis?

"Numerical attributes" are used in various statistical analyses, such as regression, correlation, and clustering, to understand patterns and relationships within the data.

What's the difference between "numerical attributes" and categorical attributes?

"Numerical attributes" represent quantifiable data, while categorical attributes represent descriptive labels or categories. For example, age is a numerical attribute, while gender is a categorical attribute.

What are some examples of preprocessing techniques for "numerical attributes"?

Common preprocessing techniques include scaling (e.g., standardization, min-max scaling), handling missing values (e.g., imputation), and transforming skewed distributions (e.g., log transformation).

Which is a better way to describe quantifiable data, "numerical attributes" or "quantitative characteristics"?

Both terms are suitable and often interchangeable. "Numerical attributes" is more common in data science and machine learning, while "quantitative characteristics" might be preferred in a more general scientific context. The best choice depends on the specific field and audience.