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Last update: February 19, 2014
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Variable Importance
GeneXproTools uses a sophisticated stochastic method to compute the
variable importance
of all the variables in a model. For all kinds of models (classification, logistic regression,
regression, time series prediction and logic synthesis)
and for all datasets, the importance of each
model variable
is computed by randomizing its input values and then computing the decrease in
the R-square between the model output and the target. The results for all variables
are then normalized so that they add up to 1.
GeneXproTools evaluates the variable importance of all
the variables (original and derived)
in a model and shows the results in the
Statistics Report in the Data Panel. The variable importance is also shown graphically in the
Variable Importance Chart in the Data Panel.
The Variable Importance Chart is available through the
Statistics Charts in the Data Panel. By selecting
Model Variables in the Variables combobox, you can quickly access
the variables of each model and quickly visualize their relative importance
in a chart. The variable importance is computed
for all the models GeneXproTools generates, including regression models, classification and
logistic regression models, and time series prediction models.
See Also:
Related Tutorials:
Related Videos:
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