ridge_py_hw4a: OLS with Ridge Regression - Python
In brian-d1018/bis557: Brian's BIS 557 Work
Description
Usage
Arguments
Value
Examples
View source: R/ridge_py_hw4a.R
Description
This function fits linear models with the ridge regression
regularization (using the l2 norm). This method is used to reduce overfitting.
Language is in Python 3.
Usage
1
ridge_py_hw4a(y, X, lambda_val)
Arguments
y
target/response variable matrix
X
independent variables matrix (contrasts should be taken care of here)
lambda_val
the lambda value (regularization parameter)
Value
the best-fit coefficients with ridge regularization
Examples
1
2
3
4
data(iris)
y <- matrix(iris$Sepal.Length, ncol = 1)
X <- model.matrix( ~ . - Sepal.Length - Species, data = iris)
ridge_py_hw4a(y, X, lambda_val = 1)
brian-d1018/bis557 documentation built on Dec. 17, 2020, 6:21 p.m.
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Description Usage Arguments Value Examples
View source: R/ridge_py_hw4a.R
Description
This function fits linear models with the ridge regression regularization (using the l2 norm). This method is used to reduce overfitting. Language is in Python 3.
Usage
1 | ridge_py_hw4a(y, X, lambda_val)
|
Arguments
y |
target/response variable matrix |
X |
independent variables matrix (contrasts should be taken care of here) |
lambda_val |
the lambda value (regularization parameter) |
Value
the best-fit coefficients with ridge regularization
Examples
1 2 3 4 | data(iris)
y <- matrix(iris$Sepal.Length, ncol = 1)
X <- model.matrix( ~ . - Sepal.Length - Species, data = iris)
ridge_py_hw4a(y, X, lambda_val = 1)
|
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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