ml_supervised_pipeline: Constructors for `ml_model` Objects

Description

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

Usage

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.