ml_metrics_binary: Extracts metrics from a fitted table

In sparklyr: R Interface to Apache Spark

Extracts metrics from a fitted table

Description

The function works best when passed a 'tbl_spark' created by 'ml_predict()'. The output 'tbl_spark' will contain the correct variable types and format that the given Spark model "evaluator" expects.

Usage

ml_metrics_binary(
  x,
  truth = label,
  estimate = rawPrediction,
  metrics = c("roc_auc", "pr_auc"),
  ...
)

Arguments

x	A 'tbl_spark' containing the estimate (prediction) and the truth (value of what actually happened)
truth	The name of the column from 'x' with an integer field containing the binary response (0 or 1). The 'ml_predict()' function will create a new field named 'label' which contains the expected type and values. 'truth' defaults to 'label'.
estimate	The name of the column from 'x' that contains the prediction. Defaults to 'rawPrediction', since its type and expected values will match 'truth'.
metrics	A character vector with the metrics to calculate. For binary models the possible values are: 'roc_auc' (Area under the Receiver Operator curve), 'pr_auc' (Area under the Precesion Recall curve). Defaults to: 'roc_auc', 'pr_auc'
...	Optional arguments; currently unused.

Details

The ‘ml_metrics' family of functions implement Spark’s 'evaluate' closer to how the 'yardstick' package works. The functions expect a table containing the truth and estimate, and return a 'tibble' with the results. The 'tibble' has the same format and variable names as the output of the 'yardstick' functions.

Examples

## Not run: 
sc <- spark_connect("local")
tbl_iris <- copy_to(sc, iris)
prep_iris <- tbl_iris %>%
  mutate(is_setosa = ifelse(Species == "setosa", 1, 0))
iris_split <- sdf_random_split(prep_iris, training = 0.5, test = 0.5)
model <- ml_logistic_regression(iris_split$training, "is_setosa ~ Sepal_Length")
tbl_predictions <- ml_predict(model, iris_split$test)
ml_metrics_binary(tbl_predictions)

## End(Not run)

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