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rprops / Phenoflow_package

R package offering functionality for the advanced analysis of microbial flow cytometry data
GNU General Public License v2.0
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Implement feature importance evaluation #48

Closed rprops closed 5 years ago

rprops commented 6 years ago

From Caret documentation for RandomForest feature importances:

"Random Forest: from the R package: “For each tree, the prediction accuracy on the out-of-bag portion of the data is recorded. Then the same is done after permuting each predictor variable. The difference between the two accuracies are then averaged over all trees, and normalized by the standard error. For regression, the MSE is computed on the out-of-bag data for each tree, and then the same computed after permuting a variable. The differences are averaged and normalized by the standard error. If the standard error is equal to 0 for a variable, the division is not done.”"

rprops commented 5 years ago

This is available from varImp() in caret package. no need for additional function in Phenoflow