ml_linear_svc
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 = 100L, standardization = TRUE, weight_col = NULL,
tol = 1e-06, threshold = 0, aggregation_depth = 2L,
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 atbl_spark
.- formula
Used when
x
is atbl_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
: Whenx
is aspark_connection
, the function returns an instance of aml_predictor
object. The object contains a pointer to a SparkPredictor
object and can be used to composePipeline
objects.ml_pipeline
: Whenx
is aml_pipeline
, the function returns aml_pipeline
with the predictor appended to the pipeline.tbl_spark
: Whenx
is atbl_spark
, a predictor is constructed then immediately fit with the inputtbl_spark
, returning a prediction model.tbl_spark
, withformula
: specified Whenformula
is specified, the inputtbl_spark
is first transformed using aRFormula
transformer before being fit by the predictor. The object returned in this case is aml_model
which is a wrapper of aml_pipeline_model
.
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