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Our secondary objective was to compare the performance of linear discriminant analysis (LDA) with non-parametric classification and regression trees (CART) for these classifications.
To deal with this problem, we classified sites, lots, and technicians according to whether their observed false positive rates were ≤0.5%, >0.5% and <1.0%, or ≥1.0%, representing acceptable, borderline, and clearly unacceptable rates, and then controlled for these classifications.
The criteria for these classifications are listed in Table 1.
Only nine of the 56 parameters were required for these classifications (Table 4).
However, the cut-off points for these classifications vary and sometimes modified scores are used.
The cut-off points for these classifications were data-driven, and we aimed at obtaining groups with similar sizes.
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A full assessment of the utility of these classifications for studies of global change drivers and forest communities was beyond the scope of this study.
All subjects with headache that did not match any of these classifications for primary headache were considered to have miscellaneous headache (MiscH).
However, the problem of error estimation for these classification rules, particularly for bagging under the small-sample settings prevalent in genomics and proteomics, is not well understood.
The training and test sets for these classification experiments were taken as given in the guidelines to the INTERSPEECH 2009 Emotion Challenge [45].
For these classification purposes, a VE cyber-recruitment message is any message that attempts to persuade the reader to join a violent extremist organization.
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