R: Spark ML - Linear Regression

ml_linear_regression {sparklyr}

R Documentation

Spark ML – Linear Regression

Description

Perform regression using linear regression.

Usage

ml_linear_regression(x, formula = NULL, fit_intercept = TRUE,
  elastic_net_param = 0, reg_param = 0, max_iter = 100L,
  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.

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.

Value

The object returned depends on the class of x.