ml_logistic_regression
Spark ML -- Logistic Regression
Perform classification using logistic regression.
Usage
ml_logistic_regression(x, formula = NULL, fit_intercept = TRUE,
elastic_net_param = 0, reg_param = 0, max_iter = 100,
threshold = 0.5, thresholds = NULL, tol = 1e-06,
weight_col = NULL, aggregation_depth = 2,
lower_bounds_on_coefficients = NULL,
lower_bounds_on_intercepts = NULL,
upper_bounds_on_coefficients = NULL,
upper_bounds_on_intercepts = NULL, features_col = "features",
label_col = "label", family = "auto",
prediction_col = "prediction", probability_col = "probability",
raw_prediction_col = "rawPrediction",
uid = random_string("logistic_regression_"), ...)Arguments
- x
A
spark_connection,ml_pipeline, or atbl_spark.- formula
Used when
xis 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?
- elastic_net_param
ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
- reg_param
Regularization parameter (aka lambda)
- max_iter
The maximum number of iterations to use.
- threshold
in binary classification prediction, in range [0, 1].
- thresholds
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
p/tis predicted, wherepis the original probability of that class andtis the class's threshold.- tol
Param for the convergence tolerance for iterative algorithms.
- weight_col
The name of the column to use as weights for the model fit.
- aggregation_depth
(Spark 2.1.0+) Suggested depth for treeAggregate (>= 2).
- lower_bounds_on_coefficients
(Spark 2.2.0+) Lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.
- lower_bounds_on_intercepts
(Spark 2.2.0+) Lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression.
- upper_bounds_on_coefficients
(Spark 2.2.0+) Upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression.
- upper_bounds_on_intercepts
(Spark 2.2.0+) Upper bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal with 1 for binomial regression, or the number of classes for multinomial regression.
- 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.- family
(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_col
Prediction column name.
- probability_col
Column name for predicted class conditional probabilities.
- 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: Whenxis aspark_connection, the function returns an instance of aml_estimatorobject. The object contains a pointer to a SparkPredictorobject and can be used to composePipelineobjects.ml_pipeline: Whenxis aml_pipeline, the function returns aml_pipelinewith the predictor appended to the pipeline.tbl_spark: Whenxis atbl_spark, a predictor is constructed then immediately fit with the inputtbl_spark, returning a prediction model.tbl_spark, withformula: specified Whenformulais specified, the inputtbl_sparkis first transformed using aRFormulatransformer before being fit by the predictor. The object returned in this case is aml_modelwhich 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_linear_svc,
ml_multilayer_perceptron_classifier,
ml_naive_bayes,
ml_one_vs_rest,
ml_random_forest_classifier
Examples
# NOT RUN {
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)
partitions <- mtcars_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
mtcars_training <- partitions$training
mtcars_test <- partitions$test
lr_model <- mtcars_training %>%
ml_logistic_regression(am ~ gear + carb)
pred <- ml_predict(lr_model, mtcars_test)
ml_binary_classification_evaluator(pred)
# }
# NOT RUN {
# }