sparklyr (version 1.8.4)

ml_metrics_regression: 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_regression(
  x,
  truth,
  estimate = prediction,
  metrics = c("rmse", "rsq", "mae"),
  ...
)

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` that contains the value of what actually happened

estimate

The name of the column from `x` that contains the prediction. Defaults to `prediction`, since it is the default that `ml_predict()` uses.

metrics

A character vector with the metrics to calculate. For regression models the possible values are: `rmse` (Root mean squared error), `mse` (Mean squared error),`rsq` (R squared), `mae` (Mean absolute error), and `var` (Explained variance). Defaults to: `rmse`, `rsq`, `mae`

...

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

if (FALSE) {
sc <- spark_connect("local")
tbl_iris <- copy_to(sc, iris)
iris_split <- sdf_random_split(tbl_iris, training = 0.5, test = 0.5)
training <- iris_split$training
reg_formula <- "Sepal_Length ~ Sepal_Width + Petal_Length + Petal_Width"
model <- ml_generalized_linear_regression(training, reg_formula)
tbl_predictions <- ml_predict(model, iris_split$test)
tbl_predictions %>%
  ml_metrics_regression(Sepal_Length)
}