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_"), ... )
| x | A |
|---|---|
| formula | Used when |
| 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 |
| label_col | Label column name. The column should be a numeric column. Usually this column is output by |
| prediction_col | Prediction column name. |
| uid | A character string used to uniquely identify the ML estimator. |
| ... | Optional arguments; see Details. |
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.
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()
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") }