library(sparklyr)
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)
#> # A tibble: 1 × 1
#> Silhouette
#> <dbl>
#> 1 0.477
ml_kmeans(iris_tbl, Species ~ .) %>%
ml_evaluate(iris_tbl)
#> # A tibble: 1 × 1
#> Silhouette
#> <dbl>
#> 1 0.850
ml_bisecting_kmeans(iris_tbl, Species ~ .) %>%
ml_evaluate(iris_tbl)
#> # A tibble: 1 × 1
#> Silhouette
#> <dbl>
#> 1 0.517Evaluate the Model on a Validation Set
R/ml_evaluate.R, R/ml_evaluator.R
ml_evaluate
Description
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
| Arguments | Description |
|---|---|
| x | An ML model object or an evaluator object. |
| dataset | The dataset to be validate the model on. |