Spark ML – Linear Regression


Perform regression using linear regression.


  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_"),


Argument Description
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.


When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.


The object returned depends on the class of x.

  • spark_connection: When x is a spark_connection, the function returns an instance of a ml_estimator object. The object contains a pointer to a Spark Predictor object and can be used to compose Pipeline objects.

  • ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the predictor appended to the pipeline.

  • tbl_spark: When x is a tbl_spark, a predictor is constructed then immediately fit with the input tbl_spark, returning a prediction model.

  • tbl_spark, with formula: specified When formula is specified, the input tbl_spark is first transformed using a RFormula transformer before being fit by the predictor. The object returned in this case is a ml_model which is a wrapper of a ml_pipeline_model.


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")

See Also

See for more information on the set of supervised learning algorithms.

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()