cla_knn: K Nearest Neighbor Classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

K Nearest Neighbor Classification

Description

Classifies using the K-Nearest Neighbor algorithm. It wraps the class library.

Usage

cla_knn(attribute, slevels, k = 1)

Arguments

attribute	attribute target to model building.
slevels	possible values for the target classification.
k	a vector of integers indicating the number of neighbors to be considered.

Value

returns a knn object.

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_knn("Species", slevels, k=3)

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

model <- fit(model, train)

prediction <- predict(model, test)
predictand <- adjust_class_label(test[,"Species"])
test_eval <- evaluate(model, predictand, prediction)
test_eval$metrics

daltoolbox documentation built on Nov. 3, 2024, 9:06 a.m.