Perform regression using linear regression.

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. 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. Boolean; should the model be fit with an intercept term? ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. Regularization parameter (aka lambda) The maximum number of iterations to use. The name of the column to use as weights for the model fit. The loss function to be optimized. Supported options: "squaredError" and "huber". Default: "squaredError" Solver algorithm for optimization. Whether to standardize the training features before fitting the model. Param for the convergence tolerance for iterative algorithms. 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 column name. The column should be a numeric column. Usually this column is output by ft_r_formula. Prediction column name. A character string used to uniquely identify the ML estimator. Optional arguments; see Details.

## Value

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.

## 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.

See http://spark.apache.org/docs/latest/ml-classification-regression.html 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()

## Examples

if (FALSE) {
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")
}