Spark ML -- Logistic Regression
ml_logistic_regression(x, response, features, intercept = TRUE, alpha = 0, lambda = 0, iter.max = 100L, ml.options = ml_options(), ...)
- 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
response is a formula, it is used in preference to other
parameters to set the
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
- The maximum number of iterations to use.
- Optional arguments, used to affect the model generated. See
ml_options for more details.
- Optional arguments; currently unused.
Perform logistic regression on a Spark DataFrame.
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
alpha is set to 0, the parameterization is equivalent to
a ridge regression model.
Other Spark ML routines: