Functions for developers writing extensions for Spark ML. These functions are constructors for ml_model objects that are returned when using the formula interface.

ml_supervised_pipeline(predictor, dataset, formula, features_col, label_col)

ml_clustering_pipeline(predictor, dataset, formula, features_col)

ml_construct_model_supervised(
constructor,
predictor,
formula,
dataset,
features_col,
label_col,
...
)

ml_construct_model_clustering(
constructor,
predictor,
formula,
dataset,
features_col,
...
)

new_ml_model_prediction(
pipeline_model,
formula,
dataset,
label_col,
features_col,
...,
class = character()
)

new_ml_model(pipeline_model, formula, dataset, ..., class = character())

new_ml_model_classification(
pipeline_model,
formula,
dataset,
label_col,
features_col,
predicted_label_col,
...,
class = character()
)

new_ml_model_regression(
pipeline_model,
formula,
dataset,
label_col,
features_col,
...,
class = character()
)

new_ml_model_clustering(
pipeline_model,
formula,
dataset,
features_col,
...,
class = character()
)

## Arguments

predictor The pipeline stage corresponding to the ML algorithm. The training dataset. The formula used for data preprocessing 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 column name. The column should be a numeric column. Usually this column is output by ft_r_formula. The constructor function for the ml_model. The pipeline model object returned by ml_supervised_pipeline(). Name of the subclass.