Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1
Variable importance computation mode. More...
VariableImportanceModeId | ( | int | value | ) |
Constructs the variable importance computation mode object identifier using the provided value
value | Value corresponding to the variable importance computation mode object identifier |
Static Public Attributes | |
static final VariableImportanceModeId | none = new VariableImportanceModeId(noneId) |
static final VariableImportanceModeId | MDI = new VariableImportanceModeId(MDIId) |
static final VariableImportanceModeId | MDA_Raw = new VariableImportanceModeId(MDA_RawId) |
static final VariableImportanceModeId | MDA_Scaled = new VariableImportanceModeId(MDA_ScaledId) |
int getValue | ( | ) |
Returns the value corresponding to the variable importance computation mode object identifier
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Mean Decrease Accuracy (permutation importance). For each tree, the prediction error on the out-of-bag portion of the data is computed (error rate for classification, MSE for regression). The same is done after permuting each predictor variable. The difference between the two are then averaged over all trees.
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Mean Decrease Accuracy (permutation importance). This is MDA_Raw value normalized by its standard deviation.
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Mean Decrease Impurity. Computed as the sum of weighted impurity decreases for all nodes where the variable is used, averaged over all trees in the forest
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Do not compute
For more complete information about compiler optimizations, see our Optimization Notice.