Spark ML -- Linear Regression

Perform linear regression on a Spark DataFrame.

ml_linear_regression(x, response, features, intercept = TRUE, alpha = 0, lambda = 0, max.iter = 100L, ...)
An object coercable to a Spark DataFrame (typically, a tbl_spark).
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.
The name of features (terms) to use for the model fit.
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.
The maximum number of iterations to use.
Optional arguments; currently unused.

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

  • ml_linear_regression
Documentation reproduced from package sparklyr, version 0.3.5, License: file LICENSE

Community examples

Looks like there are no examples yet.