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With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).
In the process, it predicts some instances correctly, which generate revenue, and it makes some errors, which cost money.
True-negative rate (hbox {TN}_mathrm{rate}=frac{hbox {TN}}{hbox {FP},+,hbox {TN}}) is the percentage of negative instances correctly classified.
The accuracy measure employed for evaluating the results is the percentage of instances correctly classified, that is, for which a correct prediction was made.
True-positive rate (hbox {TP}_mathrm{rate}=frac{hbox {TP}}{hbox {TP},+,hbox {FN}}) is the percentage of positive instances correctly classified.
In addition to the error rate that increased 76.56%, the accuracy (i.e., the percentage of correctly classified instances) and recall (i.e., the percentage of instances correctly classified as cautious) stand out for decreasing by 12% and 17.65% respectively.
Similar(48)
For instance, correctly identifying the model input parameter values is a daunting task.
This classifier is updated for each arriving training instance based on whether it predicted the instance correctly or not.
The prediction market Intrade, for instance, correctly called the outcome of the 2008 presidential election.
In other words, even if the classifier classifies an instance correctly into any of the relations NTrP, NTeP and NPP they are not counted.
In the obtained partition, 92 instances are correctly assigned giving 91% as overall purity.
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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