Spark ML – Transform, fit, and predict methods (ml_ interface)

Methods for transformation, fit, and prediction. These are mirrors of the corresponding sdf-transform-methods.

is_ml_transformer(x)

is_ml_estimator(x)

ml_fit(x, dataset, ...)

# Default S3 method
ml_fit(x, dataset, ...)

ml_transform(x, dataset, ...)

ml_fit_and_transform(x, dataset, ...)

ml_predict(x, dataset, ...)

# S3 method for class 'ml_model_classification'
ml_predict(x, dataset, probability_prefix = "probability_", ...)

Arguments

x

A ml_estimator, ml_transformer (or a list thereof), or ml_model object.

dataset

A tbl_spark.

...

Optional arguments; currently unused.

probability_prefix

String used to prepend the class probability output columns.

Value

When x is an estimator, ml_fit() returns a transformer whereas ml_fit_and_transform() returns a transformed dataset. When x is a transformer, ml_transform() and ml_predict() return a transformed dataset. When ml_predict() is called on a ml_model object, additional columns (e.g. probabilities in case of classification models) are appended to the transformed output for the user's convenience.

Details

These methods are