# 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 |

formula | Used when |

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 |

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

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`

.

`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`

.

## 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 also

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_logistic_regression`

,
`ml_multilayer_perceptron_classifier`

,
`ml_naive_bayes`

,
`ml_one_vs_rest`

,
`ml_random_forest_classifier`

## Examples

```
if (FALSE) {
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)
}
```