Constructors for 'ml_model' Objects
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() )
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 |
label_col |
Label column name. The column should be a numeric column. Usually this column is output by |
constructor |
The constructor function for the 'ml_model'. |
pipeline_model |
The pipeline model object returned by 'ml_supervised_pipeline()'. |
class |
Name of the subclass. |
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