我想用caret来调优我的自定义算法的两个参数。一个参数(lambda)是数值型，另一个参数(prior)是字符型。这个参数可以取两个值：“known”或“unknown”。我已经只用lambda参数调优过算法，效果还可以。但是当我添加字符型参数(prior)时，出现了以下错误：

1: In eval(expr, envir, enclos) : model fit failed for Resample01: lambda=1, prior=unknown Error in mdp(Class = y, data = x, lambda = param$lambda, prior = param$prior, : 未找到对象 ‘assignment’

这个错误一定与指定字符型参数(prior)的方式有关。以下是我的代码：

my_mod$parameters <- data.frame(  parameter = c("lambda","prior"),  class = c("numeric", "character"),  label = c("sample_length", "prior_type"))## The grid Elementmy_mod$grid <- function(x, y, len = NULL){expand.grid(lambda=1:2,prior=c("unknown", "known"))}mygrid<-expand.grid(lambda=1:2,prior=c('unknown','known'))## The fit Elementmy_mod$fit <- function(x, y, wts, param, lev, last, classProbs, ...){   mdp(Class=y,data=x,lambda=param$lambda,prior=param$prior,info.pred ="yes")}## The predict ElementmdpPred <- function(modelFit, newdata, preProc = NULL, submodels = NULL)  predict.mdp(modelFit, newdata)my_mod$predict <- mdpPredfitControl <- trainControl(method = "cv",number = 5,repeats = 5)train(x=data, y = factor(Class),method = my_mod,trControl = fitControl, tuneGrid = mygrid)

回答：

这是因为您必须在fit函数中指定as.character(param$prior)。