624_HW8

install.packages("remotes")

## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.0'
## (as 'lib' is unspecified)

remotes::install_github("yixuan/prettydoc")

## Skipping install of 'prettydoc' from a github remote, the SHA1 (4810d4f9) has not changed since last install.
##   Use `force = TRUE` to force installation

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set.seed(200)
trainingData <- mlbench.friedman1(200, sd = 1)
## We convert the 'x' data from a matrix to a data frame
## One reason is that this will give the columns names.
trainingData$x <- data.frame(trainingData$x)  ## Look at the data using
featurePlot(trainingData$x, trainingData$y)

## or other methods. >
## This creates a list with a vector 'y' and a matrix
## of predictors 'x'. Also simulate a large test set to > ## estimate the true error rate with good precision:
testData <- mlbench.friedman1(5000, sd = 1)
testData$x <- data.frame(testData$x) 

library(caret)
knnModel <- train(x = trainingData$x, y = trainingData$y,
                  method = "knn",
                  preProc = c("center", "scale"),
                  tuneLength = 10)
knnModel

## k-Nearest Neighbors 
## 
## 200 samples
##  10 predictor
## 
## Pre-processing: centered (10), scaled (10) 
## Resampling: Bootstrapped (25 reps) 
## Summary of sample sizes: 200, 200, 200, 200, 200, 200, ... 
## Resampling results across tuning parameters:
## 
##   k   RMSE      Rsquared   MAE     
##    5  3.466085  0.5121775  2.816838
##    7  3.349428  0.5452823  2.727410
##    9  3.264276  0.5785990  2.660026
##   11  3.214216  0.6024244  2.603767
##   13  3.196510  0.6176570  2.591935
##   15  3.184173  0.6305506  2.577482
##   17  3.183130  0.6425367  2.567787
##   19  3.198752  0.6483184  2.592683
##   21  3.188993  0.6611428  2.588787
##   23  3.200458  0.6638353  2.604529
## 
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was k = 17.

knnPred <- predict(knnModel, newdata = testData$x)
## The function 'postResample' can be used to get the test set
## perforamnce values
postResample(pred = knnPred, obs = testData$y)

##      RMSE  Rsquared       MAE 
## 3.2040595 0.6819919 2.5683461

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