diff options
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala | 10 | ||||
-rw-r--r-- | python/pyspark/ml/tuning.py | 13 |
2 files changed, 19 insertions, 4 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index 520557849b..6ea52ef7f0 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -55,7 +55,11 @@ private[ml] trait CrossValidatorParams extends ValidatorParams { } /** - * K-fold cross validation. + * K-fold cross validation performs model selection by splitting the dataset into a set of + * non-overlapping randomly partitioned folds which are used as separate training and test datasets + * e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, + * each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the + * test set exactly once. */ @Since("1.2.0") class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String) @@ -188,7 +192,9 @@ object CrossValidator extends MLReadable[CrossValidator] { } /** - * Model from k-fold cross validation. + * CrossValidatorModel contains the model with the highest average cross-validation + * metric across folds and uses this model to transform input data. CrossValidatorModel + * also tracks the metrics for each param map evaluated. * * @param bestModel The best model selected from k-fold cross validation. * @param avgMetrics Average cross-validation metrics for each paramMap in diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index 298314d46c..7f967e5463 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -143,7 +143,13 @@ class ValidatorParams(HasSeed): class CrossValidator(Estimator, ValidatorParams): """ - K-fold cross validation. + + K-fold cross validation performs model selection by splitting the dataset into a set of + non-overlapping randomly partitioned folds which are used as separate training and test datasets + e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, + each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the + test set exactly once. + >>> from pyspark.ml.classification import LogisticRegression >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator @@ -260,7 +266,10 @@ class CrossValidator(Estimator, ValidatorParams): class CrossValidatorModel(Model, ValidatorParams): """ - Model from k-fold cross validation. + + CrossValidatorModel contains the model with the highest average cross-validation + metric across folds and uses this model to transform input data. CrossValidatorModel + also tracks the metrics for each param map evaluated. .. versionadded:: 1.4.0 """ |