get.importance: Get variable importance for Logistic Regression and Random...
In software-analytics/Rnalytica: An R package of JIRA defect datasets and tool suites for explainable software analytics
View source: R/get.importance.R
Description
This function makes life simple by automatically interpreting logistic regression and random forest models.
The interpretation techniques for logistic regression are ANOVA Type-I and variants of ANOVA Type-II (i.e., Wald, Chisq, and F).
The interpretation techniques for random forest are Gini and Permutation (i.e., Scaled and Raw) importance.
Usage
1
get.importance(model, normalized = F)
Arguments
model
a logistic regression or random forest model
normalized
a logical to indicate whether to normalize importance scores
software-analytics/Rnalytica documentation built on Aug. 16, 2020, 9:38 p.m.
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View source: R/get.importance.R
Description
This function makes life simple by automatically interpreting logistic regression and random forest models. The interpretation techniques for logistic regression are ANOVA Type-I and variants of ANOVA Type-II (i.e., Wald, Chisq, and F). The interpretation techniques for random forest are Gini and Permutation (i.e., Scaled and Raw) importance.
Usage
1 | get.importance(model, normalized = F)
|
Arguments
model |
a logistic regression or random forest model |
normalized |
a logical to indicate whether to normalize importance scores |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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