Sentence examples for attributes for analysis from inspiring English sources

The phrase "attributes for analysis" is correct and usable in written English.
It can be used when discussing specific characteristics or features that are being examined or evaluated in a study or assessment.
Example: "The researcher identified several key attributes for analysis in the data set, including age, income, and education level."
Alternatives: "factors for evaluation" or "characteristics for examination".

Exact(2)

Select "streak_vs_epi_sig" and "meso_vs_streak_sig" by clicking once on each in the "Expression Attributes for Analysis" text area (Additional File 3 Figure S2D).

HNB can be used to construct a high-dimensional classification model that takes into account dependencies among the attributes for analysis of complex biological -omics datasets containing dependencies of features.

Similar(58)

In addition, because the attributes appearing at higher levels in a decision tree are considered more informative [ 11], a tree can identify significant attributes for further analysis more easily than a model learned by other approaches (e.g., the conditional probabilities of a Naïve Bayesian classifier or the tuned weights of an ANN).

The added value of the RATA approach was illustrated in the sample discrimination for some food products when using the weighted attribute scores for analysis.

The results of the analysis can be encoded into the class file as attributes for the further analysis or verification on bytecode.

We thus used price and source as attributes for the conjoint analysis based on the pre-survey and that by Japan's National Tax Agency (2008).

Tweets containing the following attributes were not used for analysis: if located outside the United States, from a user with a time zone outside the US, containing less than five words, not in English, not containing ASCII characters, and those submitted through the "API".

Since these attributes are perhaps not as straightforward for analysis programs to report as the other attributes, they could be considered optional and/or set by the user after the data is published.

Due to their size and numbers of attributes, these data are suitable for analysis with machine learning methods.

This last attribute is the quantity used for analysis, representing the differential expression of a given gene under a given experimental condition, with the other factors serving to normalize the data.

Applying time-to-event (TTE) to screening for AD was undertaken to provide maximum flexibility for analysis on the attributes of screening tests and treatments that postpone disease onset.

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