ml_linear_svc: Spark ML - LinearSVC

In sparklyr: R Interface to Apache Spark

Spark ML – LinearSVC

Description

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

Usage

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

## Not run: 
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)

## End(Not run)

sparklyr documentation built on May 29, 2024, 2:58 a.m.