Spark ML – LinearSVC

Perform classification using linear support vector machines (SVM). This binary classifier optimizes the Hinge Loss using the OWLQN optimizer. Only supports L2 regularization currently.

ml_linear_svc(
  x,
  formula = NULL,
  fit_intercept = TRUE,
  reg_param = 0,
  max_iter = 100,
  standardization = TRUE,
  weight_col = NULL,
  tol = 1e-06,
  threshold = 0,
  aggregation_depth = 2,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  raw_prediction_col = "rawPrediction",
  uid = random_string("linear_svc_"),
  ...
)

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?

reg_param

Regularization parameter (aka lambda)

max_iter

The maximum number of iterations to use.

standardization

Whether to standardize the training features before fitting the model.

weight_col

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

tol

Param for the convergence tolerance for iterative algorithms.

threshold

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

aggregation_depth

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

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.

prediction_col

Prediction column name.

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. 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.

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_logistic_regression(), ml_multilayer_perceptron_classifier(), ml_naive_bayes(), ml_one_vs_rest(), ml_random_forest_classifier()

Examples

if (FALSE) { # \dontrun{
library(dplyr)

sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

partitions <- iris_tbl %>%
  filter(Species != "setosa") %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

iris_training <- partitions$training
iris_test <- partitions$test

svc_model <- iris_training %>%
  ml_linear_svc(Species ~ .)

pred <- ml_predict(svc_model, iris_test)

ml_binary_classification_evaluator(pred)
} # }