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A calculation with the I1 indicator variable returned five concentrations as useful variables for classification, those are: iron, silver, bismuth, lead and tin.
This will clarify the issue of selecting the most powerful variables for classification.
The importance of variables for classification of a single sample is provided by RF as the local importance.
Furthermore, according to RF algorithm, the two most important variables for classification were attenuation and mean, respectively.
So far, six time span measurements, three amplitude measurements, and nine slope measurements are considered to be input variables for classification.
In the Life Sciences, RF is popular because RF classification models have a high-prediction accuracy and provide information on importance of variables for classification.
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Fig. 1 Shows popular centrality measures like degree, closeness, betweeness, reach centrality, structural holes, and density for each ego and avg_stress is taken as a binary variable for classification.
The PLS-DA is a regression extension of principal component analysis for modelling the relationship between projections of dependent factors and independent responses involving a dummy variable for classification.
This is also the problem of various Maximum Relevance (MR) approaches [ 12], which select variables inefficient for classification tasks because of the undesirable redundancy in the selected set of variables [ 13].
The objective is to reduce the number of process variables needed for classification by eliminating noisy and irrelevant ones.
Another advantage of the naive Bayes classifier is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com