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订阅分配中不允许NAs:预测概率错误

nnet predict na r

0

matsuo_basho · 技术社区 · 9 年前

消息:“newdata”有5行,但找到的变量有100行

以下是一个可复制的示例:

require(nnet)

response1 <- sample(runif(100))
response2 <- 1-response1
responses <- as.matrix(data.frame(response1 = response1, response2 = response2))

train <- data.matrix(data.frame(var1 = runif(100), var2 = runif(100)))

multinom.mod <- multinom(responses ~ train)

test.df <- data.frame(var1 = runif(5), var2 = runif(5))
predict.vec <- predict(multinom.mod, test.df)

如你所见,问题是我的回答由两个变量组成。当我预测的行数少于训练集中的行数时,函数试图将训练集中的响应变量与测试集连接起来。

以下内容适用于新的预测集。然而,响应变量被视为分类变量,因此预测不正确:

require(nnet)

train <- data.frame(response1 = sample(runif(100)), response2 = 1-response1, var1 = runif(100), var2 = runif(100))

multinom.mod <- multinom(response1 + response2 ~ ., train, type = "probs")

test.df <- data.frame(var1 = runif(5), var2 = runif(5))

predict.vec <- predict(multinom.mod, test.df)

1 回复 | 直到 9 年前

1

Consistency 9 年前

predict.vec <- predict(multinom.mod, test.df, type = "probs")

否则,默认情况下,预测在类上, type = class

更新后,完整的用法(训练和预测)应如下所示:

require(nnet)

response1 <- sample(runif(100))
response2 <- 1 - response1

train <- data.frame(var1 = runif(100), var2 = runif(100))
# train with matrix
responses <- cbind(response1, response2)
multinom.mod <- multinom(responses ~ var1 + var2, train, type = "probs")
# train with category
train$response <- ifelse(response1 > response2, "response1", "response2")
multinom.mod1 <- multinom(response ~ var1 + var2, train)

test.df <- data.frame(var1 = runif(5), var2 = runif(5))
# no matter which training method you use,
# you can predict class (default) or probability
predict.cvec <- predict(multinom.mod, test.df, type = "class")
predict.pvec <- predict(multinom.mod, test.df, type = "probs")

predict.cvec1 <- predict(multinom.mod1, test.df, type = "class")
predict.pvec1 <- predict(multinom.mod1, test.df, type = "probs")