ml_evaluator

0th

Percentile

Spark ML - Evaluators

A set of functions to calculate performance metrics for prediction models. Also see the Spark ML Documentation https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.ml.evaluation.package

Usage
ml_binary_classification_evaluator(x, label_col = "label",
  raw_prediction_col = "rawPrediction", metric_name = "areaUnderROC",
  uid = random_string("binary_classification_evaluator_"), ...)

ml_binary_classification_eval(x, label_col = "label", prediction_col = "prediction", metric_name = "areaUnderROC")

ml_multiclass_classification_evaluator(x, label_col = "label", prediction_col = "prediction", metric_name = "f1", uid = random_string("multiclass_classification_evaluator_"), ...)

ml_classification_eval(x, label_col = "label", prediction_col = "prediction", metric_name = "f1")

ml_regression_evaluator(x, label_col = "label", prediction_col = "prediction", metric_name = "rmse", uid = random_string("regression_evaluator_"), ...)

Arguments
x

A spark_connection object or a tbl_spark containing label and prediction columns. The latter should be the output of sdf_predict.

label_col

Name of column string specifying which column contains the true labels or values.

raw_prediction_col

Raw prediction (a.k.a. confidence) column name.

metric_name

The performance metric. See details.

uid

A character string used to uniquely identify the ML estimator.

...

Optional arguments; currently unused.

prediction_col

Name of the column that contains the predicted label or value NOT the scored probability. Column should be of type Double.

Details

The following metrics are supported

  • Binary Classification: areaUnderROC (default) or areaUnderPR (not available in Spark 2.X.)

  • Multiclass Classification: f1 (default), precision, recall, weightedPrecision, weightedRecall or accuracy; for Spark 2.X: f1 (default), weightedPrecision, weightedRecall or accuracy.

  • Regression: rmse (root mean squared error, default), mse (mean squared error), r2, or mae (mean absolute error.)

ml_binary_classification_eval() is an alias for ml_binary_classification_evaluator() for backwards compatibility.

ml_classification_eval() is an alias for ml_multiclass_classification_evaluator() for backwards compatibility.

Value

The calculated performance metric

Aliases
  • ml_evaluator
  • ml_binary_classification_evaluator
  • ml_binary_classification_eval
  • ml_multiclass_classification_evaluator
  • ml_classification_eval
  • ml_regression_evaluator
Examples
# NOT RUN {
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)

partitions <- mtcars_tbl %>%
  sdf_random_split(training = 0.7, test = 0.3, seed = 1111)

mtcars_training <- partitions$training
mtcars_test <- partitions$test

# for multiclass classification
rf_model <- mtcars_training %>%
  ml_random_forest(cyl ~ ., type = "classification")

pred <- ml_predict(rf_model, mtcars_test)

ml_multiclass_classification_evaluator(pred)

# for regression
rf_model <- mtcars_training %>%
  ml_random_forest(cyl ~ ., type = "regression")

pred <- ml_predict(rf_model, mtcars_test)

ml_regression_evaluator(pred, label_col = "cyl")

# for binary classification
rf_model <- mtcars_training %>%
  ml_random_forest(am ~ gear + carb, type = "classification")

pred <- ml_predict(rf_model, mtcars_test)

ml_binary_classification_evaluator(pred)
# }
# NOT RUN {
# }
Documentation reproduced from package sparklyr, version 1.0.1, License: Apache License 2.0 | file LICENSE

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