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It seems that by adding that many variables for a dataset with such a small number of examples leads to overfitting of the ANN and SVM, i.e., the models are describing the random variations in the datasets instead of the underlying general principles that exists among the variables.
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In case such examples lead to unintended results, debugging of the knowledge base is initiated.
All the above examples led to bitter conflicts further down the line.
Historically, these anecdotal examples led to conducting a series of genetic epidemiologic studies to answer specific questions into the inheritance of the presence of tics as a trait.
The above examples lead to the following immediate propositions.
This last example leads to another consideration.
The training aimed to overcome "unconscious" stereotyping which for example leads to more time spent teaching boys than girls.
"Chain" for example, leads to "chain smoke," which leads to "smoke signal," which leads to "signal light," "light beer," "beer gut" and finally "gut reaction".
Apple's decision to frequently change the design of charging ports on its cellphones and laptops, for example, leads to a lot of abandoned power cords.
Overexpression of ASIP, in mice for example, leads to synthesis of only yellow phaeomelanin, even in the presence of a functional MC1R and α-MSH8.
Minimum design achieved by application of this optimization approach to a practical design example leads to around 36% savings in cost.
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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