ml_evaluate(x, dataset)

## S3 method for class 'ml_model_logistic_regression'
ml_evaluate(x, dataset)

## S3 method for class 'ml_logistic_regression_model'
ml_evaluate(x, dataset)

## S3 method for class 'ml_model_linear_regression'
ml_evaluate(x, dataset)

## S3 method for class 'ml_linear_regression_model'
ml_evaluate(x, dataset)

## S3 method for class 'ml_model_generalized_linear_regression'
ml_evaluate(x, dataset)

## S3 method for class 'ml_generalized_linear_regression_model'
ml_evaluate(x, dataset)

## S3 method for class 'ml_model_clustering'
ml_evaluate(x, dataset)

## S3 method for class 'ml_model_classification'
ml_evaluate(x, dataset)

## S3 method for class 'ml_evaluator'
ml_evaluate(x, dataset)

## Not run: 
sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

ml_gaussian_mixture(iris_tbl, Species ~ .) %>%
  ml_evaluate(iris_tbl)

ml_kmeans(iris_tbl, Species ~ .) %>%
  ml_evaluate(iris_tbl)

ml_bisecting_kmeans(iris_tbl, Species ~ .) %>%
  ml_evaluate(iris_tbl)

## End(Not run)