ml_evaluator
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 atbl_spark
containing label and prediction columns. The latter should be the output ofsdf_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) orareaUnderPR
(not available in Spark 2.X.)Multiclass Classification:
f1
(default),precision
,recall
,weightedPrecision
,weightedRecall
oraccuracy
; for Spark 2.X:f1
(default),weightedPrecision
,weightedRecall
oraccuracy
.Regression:
rmse
(root mean squared error, default),mse
(mean squared error),r2
, ormae
(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
Examples
# NOT RUN {
sc <- spark_connect(master = "local")
mtcars_tbl <- sdf_copy_to(sc, mtcars, name = "mtcars_tbl", overwrite = TRUE)
partitions <- mtcars_tbl %>%
sdf_partition(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 <- sdf_predict(mtcars_test, rf_model)
ml_multiclass_classification_evaluator(pred)
# for regression
rf_model <- mtcars_training %>%
ml_random_forest(cyl ~ ., type = "regression")
pred <- sdf_predict(mtcars_test, rf_model)
ml_regression_evaluator(pred, label_col = "cyl")
# for binary classification
rf_model <- mtcars_training %>%
ml_random_forest(am ~ gear + carb, type = "classification")
pred <- sdf_predict(mtcars_test, rf_model)
ml_binary_classification_evaluator(pred)
# }
# NOT RUN {
# }