cuda_ml_ridge {cuda.ml} | R Documentation |

## Train a linear model using ridge regression.

### Description

Train a linear model with L2 regularization.

### Usage

```
cuda_ml_ridge(x, ...)
## Default S3 method:
cuda_ml_ridge(x, ...)
## S3 method for class 'data.frame'
cuda_ml_ridge(
x,
y,
alpha = 1,
fit_intercept = TRUE,
normalize_input = FALSE,
...
)
## S3 method for class 'matrix'
cuda_ml_ridge(
x,
y,
alpha = 1,
fit_intercept = TRUE,
normalize_input = FALSE,
...
)
## S3 method for class 'formula'
cuda_ml_ridge(
formula,
data,
alpha = 1,
fit_intercept = TRUE,
normalize_input = FALSE,
...
)
## S3 method for class 'recipe'
cuda_ml_ridge(
x,
data,
alpha = 1,
fit_intercept = TRUE,
normalize_input = FALSE,
...
)
```

### Arguments

`x` |
Depending on the context: * A __data frame__ of predictors. * A __matrix__ of predictors. * A __recipe__ specifying a set of preprocessing steps * created from [recipes::recipe()]. * A __formula__ specifying the predictors and the outcome. |

`...` |
Optional arguments; currently unused. |

`y` |
A numeric vector (for regression) or factor (for classification) of desired responses. |

`alpha` |
Multiplier of the L2 penalty term (i.e., the result would become
and Ordinary Least Square model if |

`fit_intercept` |
If TRUE, then the model tries to correct for the global mean of the response variable. If FALSE, then the model expects data to be centered. Default: TRUE. |

`normalize_input` |
Ignored when |

`formula` |
A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side. |

`data` |
When a __recipe__ or __formula__ is used, |

### Value

A ridge regressor that can be used with the 'predict' S3 generic to make predictions on new data points.

### Examples

```
library(cuda.ml)
model <- cuda_ml_ridge(formula = mpg ~ ., data = mtcars, alpha = 1e-3)
cuda_ml_predictions <- predict(model, mtcars[names(mtcars) != "mpg"])
# predictions will be comparable to those from a `glmnet` model with `lambda`
# set to 2e-3 and `alpha` set to 0
# (in `glmnet`, `lambda` is the weight of the penalty term, and `alpha` is
# the elastic mixing parameter between L1 and L2 penalties.
library(glmnet)
glmnet_model <- glmnet(
x = as.matrix(mtcars[names(mtcars) != "mpg"]), y = mtcars$mpg,
alpha = 0, lambda = 2e-3, nlambda = 1, standardize = FALSE
)
glmnet_predictions <- predict(
glmnet_model, as.matrix(mtcars[names(mtcars) != "mpg"]),
s = 0
)
print(
all.equal(
as.numeric(glmnet_predictions),
cuda_ml_predictions$.pred,
tolerance = 1e-3
)
)
```

*cuda.ml*version 0.3.2 Index]