# Spark ML -- Logistic Regression

Perform classification using logistic regression.

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_"), ...)

## 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.

## Value

The object returned depends on the class of x.

• spark_connection: When x is a spark_connection, the function returns an instance of a ml_predictor object. The object contains a pointer to a Spark Predictor object and can be used to compose Pipeline objects.

• ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the predictor appended to the pipeline.

• tbl_spark: When x is a tbl_spark, a predictor is constructed then immediately fit with the input tbl_spark, returning a prediction model.

• tbl_spark, with formula: specified When formula is specified, the input tbl_spark is first transformed using a RFormula transformer before being fit by the predictor. The object returned in this case is a ml_model which is a wrapper of a ml_pipeline_model.

## Details

When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.

See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.

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

# NOT RUN {
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)

partitions <- mtcars_tbl %>%
sdf_partition(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 <- sdf_predict(mtcars_test, lr_model)

ml_binary_classification_evaluator(pred)
# }