ml-model-constructors: Constructors for 'ml_model' Objects

In sparklyr: R Interface to Apache Spark

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.

sparklyr documentation built on May 29, 2024, 2:58 a.m.