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The goal of SVM is to construct a classifier that classifies the data instances in the testing data.
A decision tree classifies the data by following a path through each node.
When annotators agree perfectly or when a model perfectly classifies the data, Alpha (=1).
First, we consider each individual indicator separately and ask how well each chronology classifies the data into the two empirical expansion/recession distributions.
Upon considering the data characteristics in health monitoring, various data flows, heterogeneous data arrival rates, and correlation between sensor nodes, TTR MAC classifies the data into periodic data and burst data.
Random forest classifies the data with a sensitivity of 0.823 and a specificity of 0.770 (see Additional file 3: Table S9) which yields an (AUC_{random}) of 0.797 and an (AUC_{max}) of 0.959.
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Finally, we use a Random Forest (RF) classifier to classify the data according to the Association for the Advancement of Medical Instrumentation AAMII) standards (1988).
Most decision tree classifiers are designed to classify the data with categorical or Boolean class labels.
Study shows a library of features, and classifiers are available to classify the data.
These examples provide particular situations where low-level classifiers by themselves have trouble to correctly classify the data items in the test set.
To account for this variability, we classified the data set of pneumatic controllers emissions into the main controller applications: separator, process heater, compressor, dehydrator, wellhead and plunger lift.
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CEO of Professional Science Editing for Scientists @ prosciediting.com