lm_graddescent_xval: Linear model using gradient descent, adjusted for...
In kimgannon/bis557: Contains Homework Assignments for BIS 557 2020
Description
Usage
Arguments
Examples
View source: R/lm_graddescent_xval.R
Description
This function creates a linear model using gradient descent, adjusting the penalty for out-of-sample accuracy.
Usage
1
lm_graddescent_xval(f, d, gamma, n, flds)
Arguments
f
a formula the data will be fit too
d
the data to be fit (a data frame)
gamma
the step size (a positive number)
n
number of iterations (a positive integer)
flds
a list of folds
Examples
1
2
3
4
data("iris")
irisform <- Sepal.Length ~.
irisfold <- rsample::vfold_cv(iris)
lm_graddescent_xval(irisform, iris, 0.0001, 100000, irisfold)
kimgannon/bis557 documentation built on Nov. 25, 2020, 7:09 a.m.
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Description Usage Arguments Examples
View source: R/lm_graddescent_xval.R
Description
This function creates a linear model using gradient descent, adjusting the penalty for out-of-sample accuracy.
Usage
1 | lm_graddescent_xval(f, d, gamma, n, flds)
|
Arguments
f |
a formula the data will be fit too |
d |
the data to be fit (a data frame) |
gamma |
the step size (a positive number) |
n |
number of iterations (a positive integer) |
flds |
a list of folds |
Examples
1 2 3 4 | data("iris")
irisform <- Sepal.Length ~.
irisfold <- rsample::vfold_cv(iris)
lm_graddescent_xval(irisform, iris, 0.0001, 100000, irisfold)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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