Perform logistic regression on a Spark DataFrame.

ml_logistic_regression(x, response, features, intercept = TRUE, alpha = 0,
lambda = 0, 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. |

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_linear_regression`

,
`ml_multilayer_perceptron`

,
`ml_naive_bayes`

,
`ml_one_vs_rest`

, `ml_pca`

,
`ml_random_forest`

,
`ml_survival_regression`