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We used the EEG time series database [19] which is publically available and is considered as a benchmark of testing classification techniques.
The diversity of heart beat types in the multi parameter dataset is limited, which makes it less than ideal for testing classification accuracy.
This history may be available or can be included in the design of the cross-sectional study and can complement the testing classification.
A bHLH sequence dataset for testing classification models was generated as follows.
Table 3 lists the average successful testing classification rate and the standard derivation of the 6 classifiers among all trials.
To assure that each instrument was once used for training and once for testing, classification was conducted twice and accuracies of both turns were averaged.
Table 1 shows the comparisons of the average successful testing classification rate (Rate) and its standard derivation (Dev) among multiple trials for all the classifiers.
For building and testing classification models, only data from individual mice with complete data – i.e. all 15 molecular features – were used.
The one study evidencing differences in SASSI test classifications as a function of ethnicity used the original adolescent SASSI to screen learning-disabled students for chemical dependence [ 13].
First, we use 20%% of training data to build the model and test classification correction.
These are used to train and test classification and regression tree (CART).
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CEO of Professional Science Editing for Scientists @ prosciediting.com