Physical Sciences and Mathematics Commons^™

Rfviz: An Interactive Visualization Package For Random Forests In R, Christopher Beckett

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Random forests are very popular tools for predictive analysis and data science. They work for both classification (where there is a categorical response variable) and regression (where the response is continuous). Random forests provide proximities, and both local and global measures of variable importance. However, these quantities require special tools to be effectively used to interpret the forest. Rfviz is a sophisticated interactive visualization package and toolkit in R, specially designed for interpreting the results of a random forest in a user-friendly way. Rfviz uses a recently developed R package (loon) from the Comprehensive R Archive Network (CRAN) to create …

Interactive Random Forests Plots, Anna T. Quach

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Random Forests is a useful data mining tool that is quite popular in finding variable importance. However, many people don’t make use of the Random Forests results in interactive graphs. Partly, this is because software packages that can do interactive graphs can’t handle large data sets and those that use Random Forests have large data sets or many variables. A new software package in R, known as iPlots eXtreme, that is still in development makes it simple to explore large data sets interactively. I have created a function, called irfplot (interactive random forests plot) that specifically uses Random Forests to …

Investigations Of Variable Importance Measures Within Random Forests, Andrew C. Merrill

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Random Forests (RF) (Breiman 2001; Breiman and Cutler 2004) is a completely nonparametric statistical learning procedure that may be used for regression analysis and. A feature of RF that is drawing a lot of attention is the novel algorithm that is used to evaluate the relative importance of the predictor/explanatory variables. Other machine learning algorithms for regression and classification, such as support vector machines and artificial neural networks (Hastie et al. 2009), exhibit high predictive accuracy but provide little insight into predictive power of individual variables. In contrast, the permutation algorithm of RF has already established a track record for …

Comparison Of Random Forests And Cforest: Variable Importance Measures And Prediction Accuracies, Rong Xia

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Random forests are ensembles of trees that give accurate predictions for regression, classification and clustering problems. The CART tree, the base learn er employed by random forests, has been criticized because of bias in the selection of splitting variables. The performance of random forests is suspect due to this criticism. A new implementation of random forests, Cforest, which is claimed to outperform random forests in both predictive power and variable importance measures , was developed based on Ctree, an implementation of conditional inference trees.

We address the underlying mechanism of random forests and Cforest in this report. Comparison of random …

Factors Influencing Epiphytic Lichen Communities In Aspen-Associated Forests Of The Bear River Range, Idaho And Utah, Paul C. Rogers

All U.S. Government Documents (Utah Regional Depository)

In western North America, quaking aspen (Populus tremuloides Michx.) is the most common hardwood in montane landscapes. Fire suppression, grazing, wildlife management practices, and climate patterns of the past century are some of the threats to aspen coverage in this region. Researchers are concerned that aspen-dependent species may be losing habitat, thereby threatening their long-term local and regional viability. Though lichens have a rich history as air pollution indicators, I believe that they may also be useful as a metric of community diversity associated with habitat change. To date, few studies have specifically examined the status of aspen’s epiphytic lichen …

Effects Of Burning Moist Fuels On Seedbed Preparation In Cutover Western Larch Forests, United States Department Of Agriculture, Forest Service

Forestry

Natural seeding is normally the preferred method of regenerating conifers in the Northern Rocky Mountains (Schmidt and Shearer 1973). Some seedbed preparation is usually necessary for successful natural regeneration. Shade intolerant species such as western larch (Larix occidentalis Nutt.) regenerate best on bare mineral soil.