Constructors for ml_model Objects

R/ml_model_helpers.R, R/ml_model_constructors.R

ml_supervised_pipeline

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

Arguments	Description
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.