Spark ML - Isotonic Regression
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
| Arguments | Description |
|---|---|
| x | A spark_connection, ml_pipeline, or a tbl_spark. |
| formula | Used when x is a tbl_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. |
| feature_index | Index of the feature if features_col is a vector column (default: 0), no effect otherwise. |
| 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 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. |
| 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
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