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There can be a "true" probability for every outcome class that one would like to estimate.
It is also possible with some estimation procedures to proceed directly to the outcome class.
Consequently, the height of the green bars in Fig. 8 illustrates the importance of a dimension for an outcome class.
The color in the corresponding MDS projection represents again the purity of a cluster w.r.t. to one outcome class.
The goal was to assign one such outcome class to each inmate when a parole was being considered.
There also can be interest in the conditional outcome class itself: g k i = f(X i ).f.f
Similar(35)
Using the OOB data, actual outcome classes are cross-tabulated against forecasted outcome classes.
There can be similar reasoning for the outcome classes themselves.
As a consequence, the subspaces for both outcome classes are similar to each other.
Because there are three outcome classes, there are three such figures.
For instance, in the Kohonen network, the training phase usually starts by giving the initial weights which control the order of the outcome classes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com