sparklyr (version 1.7.1)

ml_evaluate: Evaluate the Model on a Validation Set

Description

Compute performance metrics.

Usage

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

# 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)
# }
# NOT RUN {
# }