similarity_measures_regression | R Documentation |

Functions that provide objects with functionality used by
`stability`

to measure the similarity between numeric
predictions of two results in regression problems.

```
edist()
msdist()
rmsdist()
madist()
qadist(p = 0.95)
cprob(kappa = 0.1)
rbfkernel()
tanimoto()
cosine()
ccc()
pcc()
```

`p` |
A numeric value between 0 and 1 specifying the probability to which the sample quantile of the absolute distance between the predictions is computed. |

`kappa` |
A positive numeric value specifying the upper limit of the absolute distance between the predictions to which the coverage probability is computed. |

The similarity measure functions provide objects that include functionality
used by `stability`

to measure the similarity between numeric
predictions of two results in regression problems.

The `edist`

(euclidean distance), `msdist`

(mean squared distance),
`rmsdist`

(root mean squared distance), `madist`

(mean absolute
distance) and `qadist`

(quantile of absolute distance) functions
implement scale-variant distance measures that are unbounded.

The `cprob`

(coverage probability), `rbfkernel`

(gaussian radial
basis function kernel), `tanimoto`

(tanimoto coefficient) and
`cosine`

(cosine similarity) functions implement scale-variant distance
measures that are bounded.

The `ccc`

(concordance correlation coefficient) and `pcc`

(pearson
correlation coefficient) functions implement scale-invariant distance
measures that are bounded between 0 and 1.

`stability`

```
set.seed(0)
library("partykit")
airq <- subset(airquality, !is.na(Ozone))
m1 <- ctree(Ozone ~ ., data = airq[sample(1:nrow(airq), replace = TRUE),])
m2 <- ctree(Ozone ~ ., data = airq[sample(1:nrow(airq), replace = TRUE),])
p1 <- predict(m1)
p2 <- predict(m2)
## euclidean distance
m <- edist()
m$measure(p1, p2)
## mean squared distance
m <- msdist()
m$measure(p1, p2)
## root mean squared distance
m <- rmsdist()
m$measure(p1, p2)
## mean absolute istance
m <- madist()
m$measure(p1, p2)
## quantile of absolute distance
m <- qadist()
m$measure(p1, p2)
## coverage probability
m <- cprob()
m$measure(p1, p2)
## gaussian radial basis function kernel
m <- rbfkernel()
m$measure(p1, p2)
## tanimoto coefficient
m <- tanimoto()
m$measure(p1, p2)
## cosine similarity
m <- cosine()
m$measure(p1, p2)
## concordance correlation coefficient
m <- ccc()
m$measure(p1, p2)
## pearson correlation coefficient
m <- pcc()
m$measure(p1, p2)
```

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

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