ml_isotonic_regression {sparklyr} | R Documentation |
Spark ML – Isotonic Regression
Description
Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported.
Usage
ml_isotonic_regression(
x,
formula = NULL,
feature_index = 0,
isotonic = TRUE,
weight_col = NULL,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
uid = random_string("isotonic_regression_"),
...
)
Arguments
x |
A |
formula |
Used when |
feature_index |
Index of the feature if |
isotonic |
Whether the output sequence should be isotonic/increasing (true) or antitonic/decreasing (false). Default: true |
weight_col |
The name of the column to use as weights for the model fit. |
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. |
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
. If it is a
spark_connection
, the function returns a ml_estimator
object. If
it is a ml_pipeline
, it will return a pipeline with the predictor
appended to it. If a tbl_spark
, it will return a tbl_spark
with
the predictions added to it.
See Also
Other ml algorithms:
ml_aft_survival_regression()
,
ml_decision_tree_classifier()
,
ml_gbt_classifier()
,
ml_generalized_linear_regression()
,
ml_linear_regression()
,
ml_linear_svc()
,
ml_logistic_regression()
,
ml_multilayer_perceptron_classifier()
,
ml_naive_bayes()
,
ml_one_vs_rest()
,
ml_random_forest_classifier()
Examples
## Not run:
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
partitions <- iris_tbl %>%
sdf_random_split(training = 0.7, test = 0.3, seed = 1111)
iris_training <- partitions$training
iris_test <- partitions$test
iso_res <- iris_tbl %>%
ml_isotonic_regression(Petal_Length ~ Petal_Width)
pred <- ml_predict(iso_res, iris_test)
pred
## End(Not run)