Spark ML -- Logistic Regression
Perform classification using logistic regression.
ml_logistic_regression(x, formula = NULL, fit_intercept = TRUE, elastic_net_param = 0, reg_param = 0, max_iter = 100L, threshold = 0.5, thresholds = NULL, tol = 1e-06, weight_col = NULL, aggregation_depth = 2L, features_col = "features", label_col = "label", family = "auto", prediction_col = "prediction", probability_col = "probability", raw_prediction_col = "rawPrediction", uid = random_string("logistic_regression_"), ...)
Boolean; should the model be fit with an intercept term?
ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
Regularization parameter (aka lambda)
The maximum number of iterations to use.
in binary classification prediction, in range [0, 1].
Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value
Param for the convergence tolerance for iterative algorithms.
The name of the column to use as weights for the model fit.
(Spark 2.1.0+) Suggested depth for treeAggregate (>= 2).
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 column name. The column should be a numeric column. Usually this column is output by
(Spark 2.1.0+) Param for the name of family which is a description of the label distribution to be used in the model. Supported options: "auto", "binomial", and "multinomial."
Prediction column name.
Column name for predicted class conditional probabilities.
Raw prediction (a.k.a. confidence) column name.
A character string used to uniquely identify the ML estimator.
Optional arguments; currently unused.
The object returned depends on the class of
spark_connection, the function returns an instance of a
ml_predictorobject. The object contains a pointer to a Spark
Predictorobject and can be used to compose
ml_pipeline, the function returns a
ml_pipelinewith the predictor appended to the pipeline.
tbl_spark, a predictor is constructed then immediately fit with the input
tbl_spark, returning a prediction model.
formula: specified When
formulais specified, the input
tbl_sparkis first transformed using a
RFormulatransformer before being fit by the predictor. The object returned in this case is a
ml_modelwhich is a wrapper of a
See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.
Other ml algorithms: