weighted knn model, with k=1:15 and three kernels

knnWeighted_kaggleDigitRecognition

# weighted k-nearest neighbors package
library(kknn)

# load the training data set
train <- read.csv("train.csv", header=TRUE)

# remove near-zero variance features
library(caret)
badCols <- nearZeroVar(train[, -1])
train <- train[, -(badCols+1)]

# optimize knn for k=1:15
# and kernel=triangular, rectangular, or gaussian
model <- train.kknn(as.factor(label) ~ ., train, kmax=15, kernel=c("triangular","rectangular","gaussian"))

# print out best parameters and prediction error
print(paste("Best parameters:", "kernel =", model$best.parameters$kernel, ", k =", model$best.parameters$k))
print(model$MISCLASS)