| ml_evaluator {sparklyr} | R Documentation |
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
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_"), ...)
x |
A |
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
|
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.
The calculated performance metric