ml_evaluate: Evaluate the Model on a Validation Set

In sparklyr: R Interface to Apache Spark

Evaluate the Model on a Validation Set

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

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)

## End(Not run)

sparklyr documentation built on Nov. 2, 2023, 5:09 p.m.