smoothing: Smoothing (binning/quantization)

In daltoolbox: Leveraging Experiment Lines to Data Analytics

Smoothing (binning/quantization)

Description

Family of smoothing methods that reduce noise by replacing values with the mean of a bin/cluster. Supported strategies: equal‑interval bins, equal‑frequency (quantile) bins, and clustering‑based bins (k‑means).

Usage

smoothing(n)

Arguments

n	number of bins

Details

The smoothing level is controlled by n (number of bins/levels). The helper tune() can choose an n by locating the elbow (maximum curvature) of the MSE curve across candidates. After fit(), values are mapped to bin means via transform().

Value

returns an object of class smoothing

Examples

data(iris)
obj <- smoothing_inter(n = 2)
obj <- fit(obj, iris$Sepal.Length)
sl.bi <- transform(obj, iris$Sepal.Length)
table(sl.bi)
obj$interval

entro <- evaluate(obj, as.factor(names(sl.bi)), iris$Species)
entro$entropy

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