sparklyr (version 1.8.5)

ml_logistic_regression: Spark ML -- Logistic Regression

Description

Perform classification using logistic regression.

Usage

ml_logistic_regression(
  x,
  formula = NULL,
  fit_intercept = TRUE,
  elastic_net_param = 0,
  reg_param = 0,
  max_iter = 100,
  threshold = 0.5,
  thresholds = NULL,
  tol = 1e-06,
  weight_col = NULL,
  aggregation_depth = 2,
  lower_bounds_on_coefficients = NULL,
  lower_bounds_on_intercepts = NULL,
  upper_bounds_on_coefficients = NULL,
  upper_bounds_on_intercepts = NULL,
  features_col = "features",
  label_col = "label",
  family = "auto",
  prediction_col = "prediction",
  probability_col = "probability",
  raw_prediction_col = "rawPrediction",
  uid = random_string("logistic_regression_"),
  ...
)

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.

Arguments

x

A spark_connection, ml_pipeline, or a tbl_spark.

formula

Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.

fit_intercept

Boolean; should the model be fit with an intercept term?

elastic_net_param

ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

reg_param

Regularization parameter (aka lambda)

max_iter

The maximum number of iterations to use.

threshold

in binary classification prediction, in range [0, 1].

thresholds

Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

tol

Param for the convergence tolerance for iterative algorithms.

weight_col

The name of the column to use as weights for the model fit.

aggregation_depth

(Spark 2.1.0+) Suggested depth for treeAggregate (>= 2).

lower_bounds_on_coefficients

(Spark 2.2.0+) Lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.

lower_bounds_on_intercepts

(Spark 2.2.0+) Lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression.

upper_bounds_on_coefficients

(Spark 2.2.0+) Upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.

upper_bounds_on_intercepts

(Spark 2.2.0+) Upper bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression.

features_col

Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula.

label_col

Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula.

family

(Spark 2.1.0+) Param for the name of family which is a description of the label distribution to be used in the model. Supported options: "auto", "binomial", and "multinomial."

prediction_col

Prediction column name.

probability_col

Column name for predicted class conditional probabilities.

raw_prediction_col

Raw prediction (a.k.a. confidence) column name.

uid

A character string used to uniquely identify the ML estimator.

...

Optional arguments; see Details.

See Also

Other ml algorithms: ml_aft_survival_regression(), ml_decision_tree_classifier(), ml_gbt_classifier(), ml_generalized_linear_regression(), ml_isotonic_regression(), ml_linear_regression(), ml_linear_svc(), ml_multilayer_perceptron_classifier(), ml_naive_bayes(), ml_one_vs_rest(), ml_random_forest_classifier()

Examples

if (FALSE) {
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)

partitions <- mtcars_tbl %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

mtcars_training <- partitions$training
mtcars_test <- partitions$test

lr_model <- mtcars_training %>%
  ml_logistic_regression(am ~ gear + carb)

pred <- ml_predict(lr_model, mtcars_test)

ml_binary_classification_evaluator(pred)
}