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_"), ...)
A spark_connection
object or a tbl_spark
containing label and prediction columns. The latter should be the output of sdf_predict
.
Name of column string specifying which column contains the true labels or values.
Raw prediction (a.k.a. confidence) column name.
The performance metric. See details.
A character string used to uniquely identify the ML estimator.
Optional arguments; currently unused.
Name of the column that contains the predicted
label or value NOT the scored probability. Column should be of type
Double
.
The calculated performance metric
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.