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The difficulty of the training task stems from the fact that a finite number of examples are available, while the resulting classifier should be optimal for all possible objects: there is a risk that the classifier classify correctly all available examples but perform poorly on other objects of the class.
The chapter also focuses on a more general problem in which a linear classifier cannot classify correctly all vectors, but seeks ways to design an optimal linear classifier by adopting an appropriate optimality criterion.
MCC is also a measure of accuracy designed to take into account the ability of a classifier to classify correctly both positive and negative instances.
Here are two examples of labeled tweets, both of which the system was able to classify correctly.
The models (including nonstochastic and stochastic indices) classify correctly more than 90% of 1525 compounds in training sets.
The validated models (including non-stochastic and stochastic indices) classify correctly more than 90% of compounds in both training and external prediction data sets.
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Only 30 of the 70 listed states are classified correctly.
The multi-PNN structure classified correctly all subjects.
Overall, 79% of the cases were classified correctly by genotype, and 85% were classified correctly by phenotype.
As a result, each document has a chance of being classified correctly for some training sets.
For eggs, the required 80% correctly classified pixels was almost reached with 79.9% of the pixels classified correctly.
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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