cla_majority: Majority Classification

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Majority Classification

Description

This function creates a classification object that uses the majority vote strategy to predict the target attribute. Given a target attribute, the function counts the number of occurrences of each value in the dataset and selects the one that appears most often.

Usage

cla_majority(attribute, slevels)

Arguments

attribute	attribute target to model building.
slevels	Possible values for the target classification.

Value

Returns a classification object.

Examples

data(iris)
slevels <- levels(iris$Species)
model <- cla_majority("Species", slevels)

# 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 May 29, 2024, 1:57 a.m.