R: Random Forests Model Selection and Performance Evaluation

# Random Forests Model Selection and Performance Evaluation

## Documentation for package ‘rfUtilities’ version 2.1-5

- DESCRIPTION file.
- Package NEWS.

## Help Pages

accuracy | Accuracy
bivariate.partialDependence | Bivariate partial-dependency plot
logLoss | Logarithmic loss (logLoss)
multi.collinear | Multi-collinearity test
occurrence.threshold | Test occurrence probability thresholds
plot.occurrence.threshold | Plot occurrence thresholds
plot.rf.cv | Plot random forests cross-validation
plot.rf.modelSel | Plot random forests model selection
plot.significance | Plot random forests significance
print.accuracy | Print accuracy
print.occurrence.threshold | Print occurrence.threshold
print.rf.cv | Print random forests cross-validation
print.rf.ensembles | Print for combined random forests ensembles
print.rf.modelSel | Print random forests model selection
print.significance | Print significance
probability.calibration | Isotonic probability calibration
rf.class.sensitivity | Random Forests class-level sensitivity analysis
rf.classBalance | Random Forest Class Balance (Zero Inflation Correction) Model
rf.combine | Combine Random Forests Ensembles
rf.crossValidation | Random Forest Classification or Regression Model Cross-validation
rf.effectSize | Random Forest effect size
rf.imp.freq | Random Forest variable selection frequency
rf.modelSel | Random Forest Model Selection
rf.partial.ci | Random Forests regression partial dependency plot with confidence intervals
rf.partial.prob | Random Forest probability scaled partial dependency plots
rf.regression.fit | Random Forest fit statistics
rf.significance | Random Forest model significance test
rf.unsupervised | Unsupervised Random Forests
rfu.news | rfUtilities news
summary.accuracy | Summarizing accuracy
summary.occurrence.threshold | Summarizing occurrence.threshold
summary.rf.cv | Summarizing cross-validation
summary.rf.ensembles | Summary for combined random forests ensembles
summary.rf.modelSel | Summarizing random forests model selection
summary.significance | Summarizing significance