Spark ML -- Logistic Regression
Perform logistic regression on a Spark DataFrame.
ml_logistic_regression(x, response, features, intercept = TRUE, alpha = 0, lambda = 0, max.iter = 100L, ...)
- An object coercable to a Spark DataFrame (typically, a
- The name of the response vector (as a length-one character
vector), or a formula, giving a symbolic description of the model to be
responseis a formula, it is used in preference to other parameters to set the
interceptparameters (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.
- 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.
Other Spark ML routines: