computeCost: computeCost
In GuangchuangYu/mlass: Machine Learning Algorithms
View source: R/LinearRegression.R
computeCost R Documentation
computeCost
Description
computing the cost J
Usage
computeCost(X, y, theta, lambda = 0)
Arguments
X
x values (a column of 1 was added)
y
y values
theta
theta values
Details
The gradient descent algorithm was to minimize the cost function.
Value
cost J
Author(s)
Guangchuang Yu https://yulab-smu.top
GuangchuangYu/mlass documentation built on Nov. 25, 2024, 4:08 a.m.
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.
View source: R/LinearRegression.R
| computeCost | R Documentation |
computeCost
Description
computing the cost J
Usage
computeCost(X, y, theta, lambda = 0)
Arguments
X |
x values (a column of 1 was added) |
y |
y values |
theta |
theta values |
Details
The gradient descent algorithm was to minimize the cost function.
Value
cost J
Author(s)
Guangchuang Yu https://yulab-smu.top
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.