我怎样才能告诉 h2o 深度学习网格有 AUC 而不是残差

Question

我想通过寻找 AUC 或准确性来衡量模型性能。在网格搜索中，我得到 residual deviance 的结果，如何告诉 h2o 深度学习网格具有 AUC 而不是残差偏差，并将结果显示为 table，如下所示？

train <- read.table(text = "target birds    wolfs     snakes
                              0        9         7 a
                              0        8         4 b
                              1        2         8 c
                              1        2         3 a
                              1        8         3 a
                              0        1         2 a
                              0        7         1 b
                              0        1         5 c
                              1        9         7 c
                              1        8         7 c
                              0        2         7 b
                              1        2         3 b
                              1        6         3 c
                              0        1         1 a
                              0        3         9 a
                              1        1         1 b ",header = TRUE)
trainHex <- as.h2o(train)

g <- h2o.grid("deeplearning",
              hyper_params = list(
                  seed = c(123456789,12345678,1234567),
                  activation = c("Rectifier", "Tanh", "TanhWithDropout", "RectifierWithDropout", "Maxout", "MaxoutWithDropout")
              ),
              reproducible = TRUE,
              x = 2:4,
              y = 1,
              training_frame = trainHex,
              validation_frame = trainHex,
              epochs = 50,
              )
g
model_ids <- g@summary_table
model_ids<-as.data.frame(model_ids)

我得到的结果table：

     Hyper-Parameter Search Summary: ordered by increasing residual_deviance
             activation      seed                                                  model_ids   residual_deviance
1                Maxout  12345678 Grid_DeepLearning_train_model_R_1483217086840_112_model_10 0.07243775676256235
2                Maxout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_16 0.10060885040861599
3     MaxoutWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_5  0.1706496158406441
4                Maxout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_4 0.17243125875659948
5                  Tanh 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_1 0.18326527198894926
6                  Tanh  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_7 0.18763395264761593
7                  Tanh   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_13 0.18791531211136187
8       TanhWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_2 0.19808063817007837
9       TanhWithDropout  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_8 0.19815190962052193
10      TanhWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_14 0.19832946889767458
11            Rectifier 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_0 0.20679125165086842
12    MaxoutWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_17 0.21971759565380736
13 RectifierWithDropout 123456789  Grid_DeepLearning_train_model_R_1483217086840_112_model_3 0.22337599298253263
14    MaxoutWithDropout  12345678 Grid_DeepLearning_train_model_R_1483217086840_112_model_11 0.22440661112729862
15 RectifierWithDropout   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_15  0.2284671685474275
16 RectifierWithDropout  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_9 0.23163744415703522
17            Rectifier   1234567 Grid_DeepLearning_train_model_R_1483217086840_112_model_12  0.2516917276707789
18            Rectifier  12345678  Grid_DeepLearning_train_model_R_1483217086840_112_model_6  0.2642221616447725

Answer 1

您可以使用 h2o.getGrid() 执行此操作。根据您的示例代码：

g_rmse <- h2o.getGrid(g@grid_id, "rmse")
g_rmse  #Output it

我在那里选择了 root-MSE。 AUC 不适用于您的示例数据：它必须是二项式分类，而您正在进行回归。

您进行回归的原因是您的 y 包含 0 和 1，因此 H2O 猜测它是数字。您需要在该列上使用 as.factor()，就在将其上传到 H2O 之后。

train <-  ...
trainHex <- as.h2o(train)
trainHex[,1] = as.factor(trainHex[,1])  #Add this

g <- ...

那么你可以这样做：

g_auc <- h2o.getGrid(g@grid_id, "auc", decreasing = TRUE)
g_auc

我已将其设置为 decreasing=TRUE，以便最佳 AUC 位于顶部。

我怎样才能告诉 h2o 深度学习网格有 AUC 而不是残差

How can I tell h2o deep learning grid to have AUC instead of residual deviance

r

auc

h2o