运行 mlr 包中的 predict() 函数出错

Question

我正在尝试运行使用 mlr 包的模型，但我在使用 predict() 函数时遇到了一些问题。它给我以下错误消息：

Error in predict(mod, task = task, subset = test) : 
Assertion on 'subset' failed: Must be of type 'integerish', not 'data.frame'

请在下面找到一个可重现的示例：

require(mlr)     # models
require(caTools) # sampling
require(Zelig)   # data

data("voteincome")
voteincome$vote <- as.factor(voteincome$vote)

set.seed(0)
sample <- sample.split(voteincome, SplitRatio = .75)
train <- subset(voteincome, sample == TRUE)
test <- subset(voteincome, sample == FALSE)

train <- na.omit(train)
test <- na.omit(test)

task <- makeClassifTask(data = train, target = "vote")
lrnr <- makeLearner("classif.randomForest")
mod <- train(lrnr, task)
pred <- predict(mod, task = task, subset = test)

然后出现错误。难道我做错了什么？谢谢！

Answer 1

试试这个：

pred <- predict(mod$learner.model, task = task, subset = test)

Answer 2

mlr 期望将索引向量传递给 subset 参数。然后它将自动对数据帧进行子集化，因此您不必自己执行此操作。您还可以使用 mlr 通过重采样描述自动将训练集和测试集分区（参见 the tutorial）：

require(mlr)     # models
require(caTools) # sampling
require(Zelig)   # data

data("voteincome")
voteincome$vote <- as.factor(voteincome$vote)

set.seed(0)
task <- makeClassifTask(data = voteincome, target = "vote")
lrnr <- makeLearner("classif.randomForest")
rdesc <- makeResampleDesc("Holdout", split = 0.75)

res <- resample(learner = lrnr, task = task, resampling = rdesc)

# get predictions on test set
getPredictionResponse(res$pred)

# compute accuracy, also see https://mlr-org.github.io/mlr-tutorial/devel/html/performance/index.html
performance(res$pred, acc)

运行 mlr 包中的 predict() 函数出错

Error while running the predict() function in mlr package

r

mlr