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2. Selecting negative examples with same size as positive examples [ 6, 7].
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The concept behind the inclusion of the retraining is that if there is more than one example with same attributes but different labels, the classifier is going to get trained to the one with most population.
Its absolute value does not matter, as long as the ordering relative to the other examples with the same qid remains the same.
Note that ranks are comparable only between examples with the same qid.
The predictive accuracy of this classifier is determined using the test (set aside) examples with the same set of features.
Carter et.al randomly selected the negative examples with the same size as the positive examples from the unlabeled examples [ 6].
However, statistics on column 1 demonstrates that the total number of estimates computed for any SIRS scenario is greater than the one for the first two examples with the same target probability and error tolerance.
For example, those with same sex partners were more likely (OR 4.84) to self-harm than those without a same sex partner.
Let's do a second example with the same sphere.
The Europa-Linofilm drum is composed of four superimposed levels, each containing 120 duplex type matrices for example, with the same letter in both roman and italic easily interchangeable in order, since their identification is not linked to their place.
For example, with the same graph of 60,000 vertices, the time decreases from 1600 to 764 s with 2 nodes and from 764 to 322 s with 3 nodes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com