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Evaluate the Model on a Validation Set

Source: R/ml_evaluate.R, R/ml_evaluator.R
ml_evaluate.Rd

Compute performance metrics.

Usage

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)

Arguments

x

An ML model object or an evaluator object.

dataset

The dataset to be validate the model on.

Examples

if (FALSE) { # \dontrun{
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)
} # }