K-Nearest Neighbor (KNN) is a supervised machine learning algorithms that can be used for classification and regression problems. In this algorithm, k is a constant defined by user and nearest neighbors distances vector is calculated by using it.

The 'caret' package provides 'knnreg' function to apply KNN for regression problems.

In this tutorial, we'll briefly learn how to fit and predict regression data by using 'knnreg' function in R. The
tutorial covers:

- Preparing the data
- Fitting the model and prediction
- Accuracy checking
- Source code listing

We'll start by loading the required libraries.

`library(caret)`

**Preparing the data**

We use Boston house-price dataset as a target regression data in this
tutorial. After loading the dataset, first, we'll split them into the
train and test parts, and extract x-input and y-label parts. Here, I'll
extract 15 percent of the dataset as test data. It is better to scale x part of data to improve the accuracy.

`boston = MASS::Boston`

str(boston)

set.seed(12)

indexes = createDataPartition(boston$medv, p = .85, list = F)

train = boston[indexes, ]

test = boston[-indexes, ]

train_x = train[, -14]

train_x = scale(train_x)[,]

train_y = train[,14]

test_x = test[, -14]

test_x = scale(test[,-14])[,]

test_y = test[,14]

` `

**Fitting the model and prediction**

We'll define the model by using the knnreg() function of the 'caret'
package and fit on train data. The
calling the function is enough to train the model with included data.

`knnmodel = knnreg(train_x, train_y)`

`str(knnmodel)`

` `

`List of 3`

$ learn :List of 2

..$ y: num [1:458] 24 21.6 34.7 33.4 36.2 28.7 16.5 18.9 15 18.9 ...

..$ X: num [1:458, 1:13] -0.418 -0.416 -0.416 -0.416 -0.411 ...

.. ..- attr(*, "dimnames")=List of 2

.. .. ..$ : chr [1:458] "1" "2" "3" "4" ...

.. .. ..$ : chr [1:13] "crim" "zn" "indus" "chas" ...

$ k : num 5

$ theDots: list()

- attr(*, "class")= chr "knnreg"

` `

Now, we can predict the x test data with the trained model.

`pred_y = predict(knnmodel, data.frame(test_x))`

**Accuracy checking**

Next, we'll check the prediction accuracy with MSE, MAE, and RMSE metrics.

`print(data.frame(test_y, pred_y))`

mse = mean((test_y - pred_y)^2)

mae = caret::MAE(test_y, pred_y)

rmse = caret::RMSE(test_y, pred_y)

cat("MSE: ", mse, "MAE: ", mae, " RMSE: ", rmse)

`MSE: 27.31944 MAE: 3.472917 RMSE: 5.2268 `

Finally, we'll visualize original test and predicted data in a plot.

`x = 1:length(test_y)`

plot(x, test_y, col = "red", type = "l", lwd=2,

main = "Boston housing test data prediction")

lines(x, pred_y, col = "blue", lwd=2)

legend("topright", legend = c("original-medv", "predicted-medv"),

fill = c("red", "blue"), col = 2:3, adj = c(0, 0.6))

grid()

` `

In this tutorial, we've learned how to fit and predict regression data
with 'knnreg' function of the 'caret' package in R. The full source code is listed below.

**Source code listing**

` `

`library(caret)`

boston = MASS::Boston

set.seed(12)

indexes = createDataPartition(boston$medv, p = .9, list = F)

train = boston[indexes, ]

test = boston[-indexes, ]

train_x = train[, -14]

train_x = scale(train_x)[,]

train_y = train[,14]

test_x = test[, -14]

test_x = scale(test[,-14])[,]

test_y = test[,14]

knnmodel = knnreg(train_x, train_y)

str(knnmodel)

pred_y = predict(knnmodel, data.frame(test_x))

mse = mean((test_y - pred_y)^2)

mae = caret::MAE(test_y, pred_y)

rmse = caret::RMSE(test_y, pred_y)

cat("MSE: ", mse, "MAE: ", mae, " RMSE: ", rmse)

x = 1:length(test_y)

plot(x, test_y, col = "red", type = "l", lwd=2,

main = "Boston housing test data prediction")

lines(x, pred_y, col = "blue", lwd=2)

legend("topright", legend = c("original-medv", "predicted-medv"),

fill = c("red", "blue"), col = 2:3, adj = c(0, 0.6))

grid()

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