# Spark ML -- Linear Regression

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. 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.

• spark_connection: When x is a spark_connection, the function returns an instance of a ml_predictor 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

# NOT RUN {
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)

partitions <- mtcars_tbl %>%
sdf_partition(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 <- sdf_predict(mtcars_test, lm_model)

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