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.

dataset

The training dataset.

formula

The formula used for data preprocessing

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.

constructor

The constructor function for the `ml_model`.

pipeline_model

The pipeline model object returned by `ml_supervised_pipeline()`.

class

Name of the subclass.