Variable importance.md

Variable importance using Random Forest in R- To determine important prognostic factors affecting breast cancer survival rate

1. Random Forest – VSURF

load the packages

library (randomForest) library (VSURF)

read file

all_data <- read.csv (file='D:/model_evaluation/all_data.csv')

run VSURF

bc.vsurf <- VSURF(bc[,1:23], bc[,24], mtry=5)

Plot Variable Importance plot

vsurf1 <- plot(bc.vsurf, step="thres", imp.sd=FALSE,var.names=TRUE)

Repeat the steps in (1) for the clusters of Data

2. Random Forest – randomForestExplainer

load the packages

library(randomForest) library(randomForestExplainer)

run Random Forest

all_data.rf <- randomForest (V24 ~. , data=all_data, ntree=500, mtry=5, keep.forest=FALSE, importance=TRUE)

Plot the variable importance with minimal depth distribution

min_depth_frame <- min_depth_distribution(all_data.rf) save(min_depth_frame, file = "min_depth_frame.rda") load("min_depth_frame.rda") head(min_depth_frame, n = 10) plot_min_depth_distribution(min_depth_frame)

Plot multi-way importance plot

importance_frame <- measure_importance(all_data.rf) save(importance_frame, file = "importance_frame.rda") load("importance_frame.rda") importance_frame (vars <- important_variables(importance_frame, k = 5, measures = c("mean_min_depth", "no_of_trees"))) plot_multi_way_importance(importance_frame, size_measure = "no_of_nodes") Repeat the steps in (2) for the clusters of Data

3. Variable importance using Random Forest

load the packages

library(randomForest) library(randomForestExplainer)

run Random Forest

all_data.rf <- randomForest (V24 ~. , data=all_data, ntree=500, mtry=5, keep.forest=FALSE, importance=TRUE)

Plot variable importance

varImpPlot(all_data.rf) Repeat the steps in (3) for the clusters of Data