View source: R/ml_regression_linear_regression.R
ml_linear_regression | R Documentation |
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
. 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.
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()
## Not run:
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")
## End(Not run)
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