# 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 = 100L,
weight_col = NULL, solver = "auto", standardization = TRUE,
tol = 1e-06, features_col = "features", label_col = "label",
prediction_col = "prediction", uid = random_string("linear_regression_"),
...)
```

## Arguments

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. |

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; currently unused. |

## 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`

.

## See also

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`