# similarity_measures_classification

0th

Percentile

##### Similarity Measure Infrastructure for Stability Assessment with Ordinal Responses

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

Keywords
stability, measures, similariy
##### 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

##### Aliases
• similarity_measures
• similarity_measures_classification
• clagree
• ckappa
• bdist
• tvdist
• hdist
• jsdiv
##### Examples
# NOT RUN {
# }
# NOT RUN {
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)

# }
# NOT RUN {
# }

Documentation reproduced from package stablelearner, version 0.1-2, License: GPL-2 | GPL-3

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