Compute performance metrics.

ml_evaluate(x, dataset)

# S3 method for ml_model_logistic_regression
ml_evaluate(x, dataset)

# S3 method for ml_logistic_regression_model
ml_evaluate(x, dataset)

# S3 method for ml_model_linear_regression
ml_evaluate(x, dataset)

# S3 method for ml_linear_regression_model
ml_evaluate(x, dataset)

# S3 method for ml_model_generalized_linear_regression
ml_evaluate(x, dataset)

# S3 method for ml_generalized_linear_regression_model
ml_evaluate(x, dataset)

# S3 method for ml_model_clustering
ml_evaluate(x, dataset)

# S3 method for ml_model_classification
ml_evaluate(x, dataset)

# S3 method for ml_evaluator
ml_evaluate(x, dataset)

Arguments

x

An ML model object or an evaluator object.

dataset

The dataset to be validate the model on.

Examples

if (FALSE) { 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) }