# ml_linear_svc

0th

Percentile

##### 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.

##### 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.

##### 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.

##### 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_estimator 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.

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

##### Aliases
• ml_linear_svc
##### 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)
# }