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| # 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 |