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Train an OLS model.

Source: R/ols.R
cuda_ml_ols.Rd

Train an ordinary least squares (OLS) model for regression tasks.

Usage

cuda_ml_ols(x, ...)

# Default S3 method
cuda_ml_ols(x, ...)

# S3 method for class 'data.frame'
cuda_ml_ols(x, y, method = c("svd", "eig", "qr"), fit_intercept = TRUE, ...)

# S3 method for class 'matrix'
cuda_ml_ols(x, y, method = c("svd", "eig", "qr"), fit_intercept = TRUE, ...)

# S3 method for class 'formula'
cuda_ml_ols(
  formula,
  data,
  method = c("svd", "eig", "qr"),
  fit_intercept = TRUE,
  ...
)

# S3 method for class 'recipe'
cuda_ml_ols(x, data, method = c("svd", "eig", "qr"), fit_intercept = TRUE, ...)

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.

method

Must be one of {"svd", "eig", "qr"}.

  • "svd": compute SVD decomposition using Jacobi iterations.

  • "eig": use an eigendecomposition of the covariance matrix.

  • "qr": use the QR decomposition algorithm and solve Rx = Q^T y.

If the number of features is larger than the sample size, then the "svd" algorithm will be force-selected because it is the only algorithm that can support this type of scenario.

Default: "svd".

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.

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, data is specified as a data frame containing the predictors and (if applicable) the outcome.

Value

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

Examples


library(cuda.ml)

if (interactive() && cuda_ml_backend_info()$runtime_installed) {
  model <- cuda_ml_ols(formula = mpg ~ ., data = mtcars, method = "qr")
  predictors <- subset(mtcars, select = -mpg)
  predictions <- predict(model, predictors)

  # predictions will be comparable to those from a `stats::lm` model
  lm_model <- stats::lm(formula = mpg ~ ., data = mtcars, method = "qr")
  lm_predictions <- predict(lm_model, predictors)

  print(
    all.equal(
      as.numeric(lm_predictions),
      predictions$.pred,
      tolerance = 1e-3
    )
  )
}