# similarity_measures_classification: Similarity Measure Infrastructure for Stability Assessment... In stablelearner: Stability Assessment of Statistical Learning Methods

 similarity_measures_classification R Documentation

## Similarity Measure Infrastructure for Stability Assessment with Ordinal Responses

### Description

Functions that provide objects with functionality used by `stability` to measure the similarity between the predictions of two results in classification problems.

### Usage

``````  clagree()
ckappa()

bdist()
tvdist()
hdist()
jsdiv(base = 2)
``````

### Arguments

 `base` A positive or complex number: the base with respect to which logarithms are computed. Defaults to 2.

### Details

The similarity measure functions provide objects that include functionality used by `stability` to measure the similarity between the probability predictions of two results in classification problems.

The `clagree` and `ckappa` functions provide an object that can be used to assess the similarity based on the predicted classes of two results. The predicted classes are selected by the class with the highest probability.

The `bdist` (Bhattacharayya distance), `tvdist` (Total variation distance), `hdist` (Hellinger distance) and `jsdist` (Jenson-Shannon divergence) functions provide an object that can be used to assess the similarity based on the predicted class probabilities of two results.

`stability`

### Examples

``````

set.seed(0)

## build trees
library("partykit")
m1 <- ctree(Species ~ ., data = iris[sample(1:nrow(iris), replace = TRUE),])
m2 <- ctree(Species ~ ., data = iris[sample(1:nrow(iris), replace = TRUE),])

p1 <- predict(m1, type = "prob")
p2 <- predict(m2, type = "prob")

## class agreement
m <- clagree()
m\$measure(p1, p2)

## cohen's kappa
m <- ckappa()
m\$measure(p1, p2)

## bhattacharayya distance
m <- bdist()
m\$measure(p1, p2)

## total variation distance
m <- tvdist()
m\$measure(p1, p2)

## hellinger distance
m <- hdist()
m\$measure(p1, p2)

## jenson-shannon divergence
m <- jsdiv()
m\$measure(p1, p2)

## jenson-shannon divergence (base = exp(1))
m <- jsdiv(base = exp(1))
m\$measure(p1, p2)

``````

stablelearner documentation built on April 14, 2023, 12:40 a.m.