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"target categories" is a correct and usable phrase in written English.
It refers to a specific group or type of people that a product, service, or message is directed towards. Here are some examples of using "target categories" in a sentence: - Our new marketing campaign will focus on targeting the millennial and Gen Z categories. - The company's demographic analysis shows that women aged 25-35 are one of their key target categories. - The survey results revealed that our product is popular among the urban and suburban target categories. - The research team identified four potential target categories for the new product launch. - The advertising team conducted a focus group with members from our target categories to gather feedback.
Exact(44)
Switching between target categories within trials slows performance.
When switches between target categories are completely random, the average number of runs should be just above 20.
While he wouldn't say what might be the next target, categories that might potentially also be included in such a strategy include fitness apps and mapping services.
During feature foraging, participants randomly switched between target categories, while during conjunction foraging, most participants repeatedly selected targets from the same category (see also Jóhannesson et al., 2016).
Furthermore, the more trials the children managed to complete during conjunction foraging, the fewer switches they made between target categories, both during feature and conjunction foraging.
Although this is nearly twice the chance level (5.2%), clearly the large number of target categories and their apparent acoustic similarities degrade the classification accuracy.
Similar(16)
But they also completed an IAT where they had to assign a series of positive or negative terms to the target category of "low carbon footprint".
The first tap from any target category (switch) is slow as shown.
Figure 4 shows clear switch-costs in ITRTs when tapping a stimulus from a different target category than the previous tap (switch) compared to ITRTs when tapping a stimulus from the same target category as the last tap (run).
A number of participants indeed voiced their intent of completing one target category before the other during feature foraging.
The Gini impurity is based on squared probabilities of membership for each target category in the node.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com