R 如何从聚合中排序输出？

以下代码：

library("C50")

portuguese_scores = read.table("https://raw.githubusercontent.com/JimGorman17/Datasets/master/student-por.csv",sep=";",header=TRUE)
portuguese_scores <- portuguese_scores[,!names(portuguese_scores) %in% c("school", "age", "G1", "G2")]
median_score <- summary(portuguese_scores$G3)['Median']
portuguese_scores$score_gte_than_median <- as.factor(median_score<=portuguese_scores$G3)
portuguese_scores <- portuguese_scores[,!names(portuguese_scores) %in% c("G3")]

set.seed(123)

train_sample <- sample(nrow(portuguese_scores), .9 * nrow(portuguese_scores))
port_train <- portuguese_scores[train_sample,]

learn_DF <- data.frame()

algorithm <- "C5.0 Decision Tree"
for (i in seq(15,100,by=1)) {
  pct_of_training_data <- sample(nrow(port_train), i/100 * nrow(port_train))
  port_train_pct <- port_train[pct_of_training_data,]

  fit <- C5.0(score_gte_than_median ~ ., data=port_train_pct)
  learn_DF <- rbind(learn_DF, data.frame(pct_of_training_set=i, err_pct=sum(predict(fit,port_train_pct) != port_train_pct$score_gte_than_median)/nrow(port_train_pct), type="train", algorithm=algorithm))
}

for (h in seq(.1, .9, by=.1)) {
  algorithm <- paste("Pruning with confidence (",h,")")  
  for (i in seq(15,100,by=1)) {
    pct_of_training_data <- sample(nrow(port_train), i/100 * nrow(port_train))
    port_train_pct <- port_train[pct_of_training_data,]

    ctrl=C5.0Control(CF=h)
    fit <- C5.0(score_gte_than_median ~ ., data=port_train_pct, ctrl=ctrl)
    learn_DF <- rbind(learn_DF, data.frame(pct_of_training_set=i, err_pct=sum(predict(fit,port_train_pct) != port_train_pct$score_gte_than_median)/nrow(port_train_pct), type="train", algorithm=algorithm))
  }
}

aggregate(err_pct~algorithm,data=learn_DF,mean)

我的问题:

• 如何按

  err\u pct

  而不是按

  算法对该网格进行排序

---

您可以将聚合结果存储在

data.frame

中，然后进行排序

res <- aggregate(err_pct~algorithm,data=learn_DF,mean)
res[order(res$err_pct), ]
                         algorithm    err_pct
2  Pruning with confidence ( 0.1 ) 0.09288930
4  Pruning with confidence ( 0.3 ) 0.09496267
10 Pruning with confidence ( 0.9 ) 0.09611947
7  Pruning with confidence ( 0.6 ) 0.09695104
6  Pruning with confidence ( 0.5 ) 0.09721156
5  Pruning with confidence ( 0.4 ) 0.09724305
9  Pruning with confidence ( 0.8 ) 0.09881957
1               C5.0 Decision Tree 0.09895810
3  Pruning with confidence ( 0.2 ) 0.09935209
8  Pruning with confidence ( 0.7 ) 0.10041991

---

res您可以使用软件包“plry”中的功能排列

库（plyr）
A.
res <- aggregate(err_pct~algorithm,data=learn_DF,mean)
res[order(res$err_pct), ]
                         algorithm    err_pct
2  Pruning with confidence ( 0.1 ) 0.09288930
4  Pruning with confidence ( 0.3 ) 0.09496267
10 Pruning with confidence ( 0.9 ) 0.09611947
7  Pruning with confidence ( 0.6 ) 0.09695104
6  Pruning with confidence ( 0.5 ) 0.09721156
5  Pruning with confidence ( 0.4 ) 0.09724305
9  Pruning with confidence ( 0.8 ) 0.09881957
1               C5.0 Decision Tree 0.09895810
3  Pruning with confidence ( 0.2 ) 0.09935209
8  Pruning with confidence ( 0.7 ) 0.10041991

library(plyr)
a<-aggregate(err_pct~algorithm,data=learn_DF,mean)
arrange(a,desc(err_pct),algorithm)