ml_generalized_linear_regression
Spark ML -- Generalized Linear Regression
Perform generalized linear regression on a Spark DataFrame.
Usage
ml_generalized_linear_regression(x, response, features, intercept = TRUE,
family = gaussian(link = "identity"), 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
responseis a formula, it is used in preference to other parameters to set theresponse,features, andinterceptparameters (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- 1in 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?
- family
The family / link function to use; analogous to those normally passed in to calls to R's own
glm.- 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_optionsfor more details.- ...
Optional arguments. The
dataargument can be used to specify the data to be used whenxis a formula; this allows calls of the formml_linear_regression(y ~ x, data = tbl), and is especially useful in conjunction withdo.
Details
In contrast to ml_linear_regression() and
ml_logistic_regression(), these routines do not allow you to
tweak the loss function (e.g. for elastic net regression); however, the model
fits returned by this routine are generally richer in regards to information
provided for assessing the quality of fit.
See Also
Other Spark ML routines: ml_als_factorization,
ml_decision_tree,
ml_gradient_boosted_trees,
ml_kmeans, ml_lda,
ml_linear_regression,
ml_logistic_regression,
ml_multilayer_perceptron,
ml_naive_bayes,
ml_one_vs_rest, ml_pca,
ml_random_forest,
ml_survival_regression