能够在分类的同时度量变量对分类的相对重要性。 本篇使用微生物群落研究中的16S扩增子测序数据,展示R包randomForest中的随机森林方法。 注:randomForest包根据经典决策树生成随机森林;如果期望根据条件推断树生成随机森林,可使用party包。当预测变量间高度相关时,基于条件推断树的随机森林可能效果更好。 示例数据,R代码的...
pfun <- function(model, newdata){ predict(model, newdata=newdata) } set.seed(1234) rf.shap <- explain(rf.model, X=Test, pred_wrapper=pfun, shap_only=F, feature_names=c("指标1", "指标2", "指标3", "指标4", "指标5", "指标6")) sv_importance(shapviz(rf.shap), kind="beeswa...
现在让我们检查一下GKtau的数值 class_list <- lapply(X = 1:ncol(dataset_2), function(x) class(dataset_2[,x])) t <- sapply(X = names(class_list) , FUN = function(x) TRUE %in% ( class_list[x] %in% c("factor","logical")) ) dataset_cat_variables_2 <- subset(x = dataset_2,...
class_list <-lapply(X =1:ncol(dataset_2),function(x)class(dataset_2[,x])) t <-sapply(X = names(class_list) , FUN =function(x) TRUE %in% ( class_list[x] %in% c("factor","logical")) ) dataset_cat_variables_2 <-subset(x = dataset_2, select = t )plot(dataset_2) 从矩阵图...
class_list <- lapply(X = 1:ncol(dataset_2), function(x) class(dataset_2[,x])) t <- sapply(X = names(class_list) , FUN = function(x) TRUE %in% ( class_list[x] %in% c("factor","logical")) ) dataset_cat_variables_2 <- subset(x = dataset_2, select = t ) ...
forest函数R包 random forest algorithm (About this article) In this article, we will try to get a deeper understanding of what each of the parameters does in the Random Forest algorithm. This is not an explanation of how the algorithm works. ( You might want to start with a simple ...
additional arguments to be passed directly to as.randomForest.rpart. Details These functions convert an existing object of class rxDForest, rxDTree, or rpart to an object of class randomForest, respectively. The underlying structure of the output object will be a subset of that produced by an...
t <- sapply(X = names(class_list) , FUN = function(x) TRUE %in% ( class_list[x] %in% c("factor","logical")) ) dataset_cat_variables_2 <- subset(x = dataset_2, select = t ) plot(dataset_2) 1. 2. 3. 4. 5. 6.
问randomForest错误: NA在预测器中不允许(但在数据中不允许NAs )EN作者简介：一名在校计算机学生、每天...
t <-sapply(X = names(class_list) , FUN =function(x) TRUE %in% ( class_list[x] %in% c("factor","logical")) ) dataset_cat_variables_2 <-subset(x = dataset_2, select = t )plot(dataset_2) 从矩阵图上的tau值及其背景形状,我们可以看到cigsPerDay可以完全解释currentSmoker的变异性。这并...