Functions for developers writing extensions for Spark ML. These functions are constructors for `ml_model` objects that are returned when using the formula interface.
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())
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, ...)
The pipeline model object returned by `ml_supervised_pipeline()`.
The formula used for data preprocessing
The training dataset.
Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula
.
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
.
Name of the subclass.
The pipeline stage corresponding to the ML algorithm.
The constructor function for the `ml_model`.