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Samples of training data (Example 1) and testing data (Example 2) are provided below.
Random Forests is an ensemble of trees created by using bootstrap samples of training data and random feature selection in tree induction [45].
After evaluating results on different patch sizes and number of samples of training data, the threshold value 0.69 was selected for the optimum performance of the algorithm.
Serum samples of training and validation sets were enriched for N-glycopeptides and analyzed using optimized SRM assays on a QQQ LC/MS instrument as described above.
The random forests algorithm builds an ensemble of decision trees, where each tree is built on bootstrap samples of training data with a randomly selected subset of factors.
To screen urine peptides of interest to diagnose CaOx nephrolithiasis, urinary samples of training set from 54 patients with CaOx nephrolithiasis and 24 healthy controls were analyzed by MALDI-TOF-MS with WCX-MB (Table 1).
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The sampling of training data for each tree facilitates finding structures that would ordinarily be overlooked.
The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability.
The study reported on here is admittedly limited in that it relied on a convenience sample of training data provided by a single online provider.
Looking at each literature's methods carefully, we found that such discrepancy was mainly due to different sampling of training data.
A sample of training materials and training aids can be found in Annexes 13 and 14 of the project's final evaluation, located at: http://caregroups.info/docs/FH_Final_Eval_Report_27Dec2010.pdf.info/docs/FH_Final_Eval_Report_27Dec2010.pdf
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com