Question

我从H2O开始，尝试在R f中集成随机森林和多元线性回归。我正在使用的H2O数据框如下：

summary(training_frame)
 HS              AS              HST              AST              HF              AF             
 Min.   : 3.00   Min.   : 2.00   Min.   : 0.000   Min.   : 0.000   Min.   : 3.00   Min.   : 1.00  
 1st Qu.:11.00   1st Qu.: 8.00   1st Qu.: 3.000   1st Qu.: 2.000   1st Qu.:11.00   1st Qu.:11.00  
 Median :14.00   Median :11.00   Median : 5.000   Median : 4.000   Median :14.00   Median :14.00  
 Mean   :14.44   Mean   :11.53   Mean   : 5.211   Mean   : 4.063   Mean   :14.39   Mean   :14.03  
 3rd Qu.:18.00   3rd Qu.:15.00   3rd Qu.: 7.000   3rd Qu.: 5.000   3rd Qu.:17.00   3rd Qu.:17.00  
 Max.   :36.00   Max.   :28.00   Max.   :18.000   Max.   :13.000   Max.   :30.00   Max.   :27.00  
 HC               AC               HY              AY              HR               AR              
 Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   :0.000   Min.   :0.0000   Min.   :0.0000  
 1st Qu.: 4.000   1st Qu.: 3.000   1st Qu.:1.000   1st Qu.:2.000   1st Qu.:0.0000   1st Qu.:0.0000  
 Median : 6.000   Median : 5.000   Median :2.000   Median :3.000   Median :0.0000   Median :0.0000  
 Mean   : 6.421   Mean   : 4.824   Mean   :2.563   Mean   :2.858   Mean   :0.1632   Mean   :0.2079  
 3rd Qu.: 8.000   3rd Qu.: 7.000   3rd Qu.:3.000   3rd Qu.:4.000   3rd Qu.:0.0000   3rd Qu.:0.0000  
 Max.   :17.000   Max.   :13.000   Max.   :8.000   Max.   :7.000   Max.   :2.0000   Max.   :3.0000  
 dif              
 Min.   :-5.0000  
 1st Qu.:-1.0000  
 Median : 0.0000  
 Mean   : 0.5026  
 3rd Qu.: 2.0000  
 Max.   : 6.0000

然后，我尝试设置两个模型和超级学习者来预测变量“dif”，代码如下：

predictores <- names(X[,-13])


regre.1 <- function(..,family = "gaussian",lambda = 0) h2o.glm.wrapper(..,family = family,lambda = lambda)

randomforest.1 <- function(...,mtries = 5,ntree = 500) h2o.randomForest.wrapper(...,mtries = mtries,ntree = ntree)

h2o.glm.1 <- function(..., family = "gaussian",lambda = 0) h2o.glm.wrapper(..., family = family,lambda = lambda)

learner <- c("regre.1", "randomforest.1")

metalearner <- "h2o.glm.1"

fit <- h2o.ensemble(x = predictores, y = "dif", 
                    training_frame = training_frame, 
                    learner = learner, 
                    metalearner = metalearner,
                    cvControl = list(V = 5))

但是，我收到此错误消息：

|============================================================================================| 100%
[1] "Cross-validating and training base learner 1: regre.1"
Error in match.fun(learner[l])(y = y, x = x, training_frame = training_frame,  : 
  unused arguments (y = y, x = x, training_frame = training_frame, validation_frame = NULL, fold_column = fold_column, keep_cross_validation_folds = TRUE)
Timing stopped at: 0 0 0

我的代码出了什么问题？

Answer 1

在regre.1中，您使用..代替...！

H20中的堆叠回归中的错误消息 - R

1 个答案: