如何在训练函数插入符号 R 中传递字符向量 [英] How to pass a character vector in the train function caret R

问题描述

我想在训练模型时减少变量的数量.我总共有 784 个特征，我想减少到 500 个.我可以用选定的特征制作一个长字符串，使用粘贴命令折叠成一个长字符串.例如，假设这是我的向量

I want to reduce the number of variables when i train my model. I have a total of 784 features that I want to reduce to lets say 500. I can make a long string with the selected featuees with the Paste command collapsed with + to have a long string. For example, lets say this is my vector

val <- "pixel40+pixel46+pixel48+pixel65+pixel66+pixel67"

然后我想像这样将它传递给火车功能

then I would like to pass it to the train function like so

Rf_model <- train(label~val, data =training, method="rf", ntree=200, na.action=na.omit)

但我收到错误

model.frame.default(form = label ~ val, data = training, na.action = na.omit)

谢谢！路易斯

推荐答案

你可以这样做:

val <- "pixel40+pixel46+pixel48+pixel65+pixel66+pixel67"

#use paste to paste the label to val
#and then use as.formula to convert to formula
form <- as.formula(paste('label ~', val))
#> form
#label ~ pixel40 + pixel46 + pixel48 + pixel65 + pixel66 + pixel67 

Rf_model <- train(form, data =training, method="rf", ntree=200, na.action=na.omit)

此外，在这种情况下，使用字符串创建公式应该没问题，因为这很简单，但对于更复杂的公式，它可能会证明容易出错.在这种情况下，您可以探索 stats::update 或 Formula 包.

Also, in this case using a string to create a formula should be fine since this is straightforward, but for more complex formulas it might prove error prone. In such cases you can explore stats::update or the Formula package.

或者你也可以使用 update(虽然我更喜欢以前的方式):

Or you could alternatively use update (although I prefer the previous way):

#> update(label ~ 1,  paste('~', val) )
#label ~ pixel40 + pixel46 + pixel48 + pixel65 + pixel66 + pixel67

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