ml_linear_regression {sparklyr} | R Documentation

## Spark ML – Linear Regression

### Description

Perform linear regression on a Spark DataFrame.

### Usage

    ml_linear_regression(x, response, features, intercept = TRUE, alpha = 0,
      lambda = 0, weights.column = NULL, iter.max = 100L,
      ml.options = ml_options(), ...)

### Arguments

`x` | An object coercable to a Spark DataFrame (typically, a `tbl_spark`).

`response`

The name of the response vector (as a length-one character vector), or a formula, giving a symbolic description of the model to be fitted. When `response` is a formula, it is used in preference to other parameters to set the `response`, `features`, and `intercept` parameters (if available). Currently, only simple linear combinations of existing parameters is supposed; e.g. `response ~ feature1 + feature2 + ...`. The intercept term can be omitted by using `- 1` in the model fit.

`features` | The name of features (terms) to use for the model fit.
`intercept` | Boolean; should the model be fit with an intercept term?
`alpha, lambda` | Parameters controlling loss function penalization (for e.g. lasso, elastic net, and ridge regression). See Details for more information.
`weights.column` | The name of the column to use as weights for the model fit.
`iter.max` | The maximum number of iterations to use.
`ml.options` | Optional arguments, used to affect the model generated. See `ml_options` for more details.
`...` | Optional arguments. The `data` argument can be used to specify the data to be used when `x` is a formula; this allows calls of the form `ml_linear_regression(y ~ x, data = tbl)`, and is especially useful in conjunction with `do`.

### Details

Spark implements for both L1 and L2 regularization in linear regression models. See the preamble in the Spark Classification and Regression documentation for more details on how the loss function is parameterized.

In particular, with `alpha` set to 1, the parameterization is equivalent to a lasso model; if `alpha` is set to 0, the parameterization is equivalent to a ridge regression model.

### See Also

Other Spark ML routines: `ml_als_factorization`, `ml_decision_tree`, `ml_generalized_linear_regression`, `ml_gradient_boosted_trees`, `ml_kmeans`, `ml_lda`, `ml_logistic_regression`, `ml_multilayer_perceptron`, `ml_naive_bayes`, `ml_one_vs_rest`, `ml_pca`, `ml_random_forest`, `ml_survival_regression`

[Package sparklyr version 0.6.3 Index]