ml-model-constructors {sparklyr} | R Documentation |
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, ...)
pipeline_model |
The pipeline model object returned by 'ml_supervised_pipeline()'. |
formula |
The formula used for data preprocessing |
dataset |
The training dataset. |
label_col |
Label column name. The column should be a numeric column. Usually this column is output by |
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 |
class |
Name of the subclass. |
predictor |
The pipeline stage corresponding to the ML algorithm. |
constructor |
The constructor function for the 'ml_model'. |