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Depending on the analogues retrieved by different similarity metrics, the learned decision boundaries and values can vary significantly.
The most frequently used features in the learned decision tree are position-specific features, local alignment features and the JOELib2 chemical features.
A potentially useful property is that learned decision trees can provide insight into what the most important features are (such insight can also be provided by dimensionality reduction methods).
Human monogamy is a choice -- a learned decision that we practice every day.
However, a cumbersome shortcoming of SVMs is that their learned decision rules are very hard to understand for humans and cannot easily be related to biological facts.
When predicting the target value for a previously unseen example, traverse the learned decision tree from the root according to the descriptive attribute values of the new example until reaching a leaf, which predicts the target attribute value.
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Spectral Relevance Analysis (SpRAy), an extension of LRP technology, identifies and quantifies a wide spectrum of learned decision-making behavior.
In this paper, we took the opposite strategy and transformed an experimental protocol used for evaluating learned decisions in humans [ 16, 17, 19] into a pigeon foraging analogue (see figure 1).
Throughout the semester, students learned decision-making choreographic skills and developed dance pieces around central ideas.
Working in movement-based laboratories throughout the semester, students have learned decision-making choreographic skills, as well as developed pieces around central ideas.
Beauty believes she made a hard-learned, measured decision in her adoption of K2 as drug of choice, one she stands by even still.
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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