rsq {mlr3measures} | R Documentation |
R Squared
Description
Measure to compare true observed response with predicted response in regression tasks.
Usage
rsq(truth, response, na_value = NaN, ...)
Arguments
truth |
( |
response |
( |
na_value |
( |
... |
( |
Details
R Squared is defined as
1 - \frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2}.
Also known as coefficient of determination or explained variation.
Subtracts the rse()
from 1, hence it compares the squared error of
the predictions relative to a naive model predicting the mean.
This measure is undefined for constant t
.
Value
Performance value as numeric(1)
.
Meta Information
Type:
"regr"
Range:
(-\infty, 1]
Minimize:
FALSE
Required prediction:
response
See Also
Other Regression Measures:
ae()
,
ape()
,
bias()
,
ktau()
,
mae()
,
mape()
,
maxae()
,
maxse()
,
medae()
,
medse()
,
mse()
,
msle()
,
pbias()
,
rae()
,
rmse()
,
rmsle()
,
rrse()
,
rse()
,
sae()
,
se()
,
sle()
,
smape()
,
srho()
,
sse()
Examples
set.seed(1)
truth = 1:10
response = truth + rnorm(10)
rsq(truth, response)