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[SPARK-15254][DOC] Improve ML pipeline Cross Validation Scaladoc & PyDoc

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## What changes were proposed in this pull request?
Updated ML pipeline Cross Validation Scaladoc & PyDoc.

## How was this patch tested?

Documentation update

(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Author: krishnakalyan3 <krishnakalyan3@gmail.com>

Closes #13894 from krishnakalyan3/kfold-cv.
This commit is contained in:
krishnakalyan3 2016-07-27 15:37:38 +02:00 committed by Nick Pentreath
parent 045fc36066
commit 7e8279fde1
2 changed files with 19 additions and 4 deletions
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10
mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
View file

@ -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

13
python/pyspark/ml/tuning.py
View file

@ -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
"""