reg_knn: K-Nearest Neighbors (KNN) Regression

In daltoolbox: Leveraging Experiment Lines to Data Analytics

K-Nearest Neighbors (KNN) Regression

Description

KNN regression using FNN::knn.reg, predicting by averaging the targets of the k nearest neighbors.

Usage

reg_knn(attribute, k)

Arguments

attribute	attribute target to model building
k	number of k neighbors

Details

Non‑parametric approach suitable for local smoothing. Sensitive to feature scaling; consider normalization beforehand.

Value

returns a knn regression object

References

Altman, N. (1992). An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression.

Examples

data(Boston)
model <- reg_knn("medv", k=3)

# preparing dataset for random sampling
sr <- sample_random()
sr <- train_test(sr, Boston)
train <- sr$train
test <- sr$test

model <- fit(model, train)

test_prediction <- predict(model, test)
test_predictand <- test[,"medv"]
test_eval <- evaluate(model, test_predictand, test_prediction)
test_eval$metrics

daltoolbox documentation built on Nov. 5, 2025, 7:09 p.m.