在这种情况下如何制作混淆矩阵？

Question

library(h2o)
h2o.init(nthreads=-1)
test <- h2o.importFile(path = "C:/Users/AkshayJ/Documents/newapril/data/testdata.csv")
train <- h2o.importFile(path = "C:/Users/AkshayJ/Documents/newapril/data/traindata.csv")
y <- "Label"
train[,y] <- as.factor(train[,y])
test[,y] <- as.factor(test[,y])
train[,"Allele1Top"] <- as.factor(train[,"Allele1Top"])
test[,"Allele1Top"] <- as.factor(test[,"Allele1Top"])
train[,"Allele2Top"] <- as.factor(train[,"Allele2Top"])
test[,"Allele2Top"] <- as.factor(test[,"Allele2Top"])
train[,"Allele1Forward"] <- as.factor(train[,"Allele1Forward"])
test[,"Allele1Forward"] <- as.factor(test[,"Allele1Forward"])
train[,"Allele2Forward"] <- as.factor(train[,"Allele2Forward"])
test[,"Allele2Forward"] <- as.factor(test[,"Allele2Forward"])
train[,"Allele1AB"] <- as.factor(train[,"Allele1AB"])
test[,"Allele1AB"] <- as.factor(test[,"Allele1AB"])
train[,"Allele2AB"] <- as.factor(train[,"Allele2AB"])
test[,"Allele2AB"] <- as.factor(test[,"Allele2AB"])
train[,"Chr"] <- as.factor(train[,"Chr"])
test[,"Chr"] <- as.factor(test[,"Chr"])
train[,"SNP"] <- as.factor(train[,"SNP"])
test[,"SNP"] <- as.factor(test[,"SNP"])
x <- setdiff(names(train),y)
model <- h2o.deeplearning(
x = x,
y = y,
training_frame = train,
validation_frame = test,
distribution = "multinomial",
activation = "RectifierWithDropout",
hidden = c(32,32,32),
input_dropout_ratio = 0.2,
sparse = TRUE,
l1 = 1e-5,
epochs = 10)
predic <- h2o.predict(model, newdata = test)
table(pred=predic, true = test[,21])

一切都很好，但最后一行 table(pred=predic, true = test[21]) 给出错误 unique.default(x, nmax = nmax) 错误：向量分配

中无效 type/length (environment/0)

Answer 1

使用函数h2o.confusionMatrix()得到混淆矩阵。简单的方法是给它模型，以及你想要分析的数据：

h2o.confusionMatrix(model, test)

如果您查看 ?h2o.confusionMatrix，您会发现它也可以接受 H2OModelMetrics 对象。您可以通过调用 h2o.performance():

获得其中之一

p = h2o.performance(model, test)
h2o.confusionMatrix(p)

我推荐第二种方式，因为 p 对象包含关于模型好坏的其他有用信息。

注意：无论哪种方式，您都没有使用您的预测。基本上：

h2o.performance如果要分析模型的好坏
h2o.predict 如果您想获得实际预测。

在这种情况下如何制作混淆矩阵？

How to make the confusion matrix in this case?

r

machine-learning

deep-learning

h2o