Provides a function to check the
importance of a feature based on a dependent classification
variable. An ensemble of correlation and importance measure
tests are used to determine the normed importance value of
all features. Combining these methods in one function
(building the sum of the importance values) leads to a better
tool for selecting most important features. This selection
can also be viewed in a barplot using the barplot_fs() function
and proved using an also provided function for a logistic
regression model, namely logreg_test().
| Version: |
1.0.0 |
| Imports: |
party, pROC, randomForest, ROCR, grDevices, graphics, stats |
| Published: |
2016-08-02 |
| Author: |
Nikita Genze, Ursula Neumann |
| Maintainer: |
Ursula Neumann <u.neumann at wz-straubing.de> |
| BugReports: |
NA |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
NA |
| NeedsCompilation: |
no |
| CRAN checks: |
EFS results |