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Spark ML – Linear Regression

Source: R/ml_regression_linear_regression.R
ml_linear_regression.Rd

Perform regression using linear regression.

Usage

ml_linear_regression(
  x,
  formula = NULL,
  fit_intercept = TRUE,
  elastic_net_param = 0,
  reg_param = 0,
  max_iter = 100,
  weight_col = NULL,
  loss = "squaredError",
  solver = "auto",
  standardization = TRUE,
  tol = 1e-06,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  uid = random_string("linear_regression_"),
  ...
)

Arguments

x

A spark_connection, ml_pipeline, or a tbl_spark.

formula

Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.

fit_intercept

Boolean; should the model be fit with an intercept term?

elastic_net_param

ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

reg_param

Regularization parameter (aka lambda)

max_iter

The maximum number of iterations to use.

weight_col

The name of the column to use as weights for the model fit.

loss

The loss function to be optimized. Supported options: "squaredError" and "huber". Default: "squaredError"

solver

Solver algorithm for optimization.

standardization

Whether to standardize the training features before fitting the model.

tol

Param for the convergence tolerance for iterative algorithms.

features_col

Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula.

label_col

Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula.

prediction_col

Prediction column name.

uid

A character string used to uniquely identify the ML estimator.

...

Optional arguments; see Details.

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.

See also

Other ml algorithms: ml_aft_survival_regression(), ml_decision_tree_classifier(), ml_gbt_classifier(), ml_generalized_linear_regression(), ml_isotonic_regression(), ml_linear_svc(), ml_logistic_regression(), ml_multilayer_perceptron_classifier(), ml_naive_bayes(), ml_one_vs_rest(), ml_random_forest_classifier()

Examples

if (FALSE) { # \dontrun{
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)

partitions <- mtcars_tbl %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

mtcars_training <- partitions$training
mtcars_test <- partitions$test

lm_model <- mtcars_training %>%
  ml_linear_regression(mpg ~ .)

pred <- ml_predict(lm_model, mtcars_test)

ml_regression_evaluator(pred, label_col = "mpg")
} # }