When a method requires a function from a certain package, that package will need to be installed. In this case, the function is the base R function glm(), so no additional package is required. The train() function is essentially a wrapper around whatever method we chose. The method essentially specifies both the model (and more specifically the function to fit said model in R) and package that will be used. method = glm specifies that we will fit a generalized linear model.trControl = trainControl(method = "cv", number = 5) specifies that we will be using 5-fold cross-validation.data = default_trn specifies that training will be down with the default_trn data.It also indicates that all available predictors should be used. specifies the default variable as the response. Here, we have supplied four arguments to the train() function form the caret package. , data = default_trn, trControl = trainControl( method = "cv", number = 5), method = "glm", family = "binomial" ) predict() used on objects of type train will be truly magical!ĭefault_glm_mod = train( form = default ~.tuneGrid which specifies the tuning parameters to train over. ![]()
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