Sentence examples for a classification task for from inspiring English sources

The phrase "a classification task for" is correct and usable in written English.
It can be used when describing a specific type of task that involves categorizing or classifying data or information based on certain criteria.
Example: "The project involves a classification task for identifying different species of plants based on their characteristics."
Alternatives: "a categorization task for" or "a sorting task for".

Exact(5)

Subsequent to learning, students performed a classification task for novel test stimuli without any support.

A classification task for phishing detection requires labeled phish examples and non-phish examples.

Consider a classification task for two classes: x1,…2,x,x N are N samples in the training set, and t1,t2,…,t N are the corresponding label, where t n ∈ {-1,+1}.

In a classification task, for example, the initial key-value pair might be a filename and a list of instances, and the intermediate output from the mapper would be a list of each instance with its associated class.

As a positive control, the entire cross-validation loop was used to classify the samples regarding the sex of the patients, a classification task for which there should exist a perfect linear separation in the normal.

Similar(55)

Furthermore, we compared calculation of the spectrograms by means of based on (Hermite) MTs to spectrograms based on a single Hanning window and concluded that MTs increase the performance in a classification task, both for AS-based and for spectrogram-based feature representation. 1 https://engineering.purdue.edu/~malcolm/interval/1998-010/.

Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables.

The finding that observers can easily shift their criterion in a classification task has implications for the use of the MSS to measure perceptual biases.

The (t) decision trees in the forest are then fitted using the (t) bootstrap samples and combined by voting for a classification task or averaging the output for regression task.

For a classification task we generate two such HMMs, one for good responders and one for poor responders.

This performance similarity for ligands can be attributed to the much simpler topologies in comparison to their binding pockets and the very small size of the benchmarking set, which renders it an easier classification task for a shape-only signature.

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