Untitled

library(tidyverse)

## -- Attaching packages -------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.2.1     v purrr   0.3.2
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## -- Conflicts ----------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(caret)

## Loading required package: lattice

## 
## Attaching package: 'caret'

## The following object is masked from 'package:purrr':
## 
##     lift

iris <- datasets::iris

Split

split <- createDataPartition(iris$Species, p = 0.7, list = F)

train <- iris[split,]
test <- iris[-split,]

Fit

fit <- train(Species ~., method = "knn", data = train)

Predict training

prediction <- predict(fit, train)

Nest

nested <- iris %>% 
  group_by(Species) %>% 
  nest() 

Write function to apply to each nested df where fit is our trained knn model

applymodel <- function(df) {
    predict(fit, df)
}

apply and view predictions

nested %>% 
  mutate(model = map(data,applymodel)) %>% 
  unnest(model) %>% 
  view()