diff options
Diffstat (limited to 'python/pyspark/ml')
-rw-r--r-- | python/pyspark/ml/classification.py | 24 | ||||
-rwxr-xr-x | python/pyspark/ml/feature.py | 64 | ||||
-rw-r--r-- | python/pyspark/ml/regression.py | 34 | ||||
-rw-r--r-- | python/pyspark/ml/tuning.py | 6 |
4 files changed, 11 insertions, 117 deletions
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index c035942f73..3c4af90aca 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -49,8 +49,6 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds, HasWeightCol, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Logistic regression. Currently, this class only supports binary classification. @@ -216,8 +214,6 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti class LogisticRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by LogisticRegression. .. versionadded:: 1.3.0 @@ -277,6 +273,8 @@ class LogisticRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): class LogisticRegressionSummary(JavaWrapper): """ + .. note:: Experimental + Abstraction for Logistic Regression Results for a given model. .. versionadded:: 2.0.0 @@ -321,6 +319,8 @@ class LogisticRegressionSummary(JavaWrapper): @inherit_doc class LogisticRegressionTrainingSummary(LogisticRegressionSummary): """ + .. note:: Experimental + Abstraction for multinomial Logistic Regression Training results. Currently, the training summary ignores the training weights except for the objective trace. @@ -501,8 +501,6 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred TreeClassifierParams, HasCheckpointInterval, HasSeed, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - `Decision tree <http://en.wikipedia.org/wiki/Decision_tree_learning>`_ learning algorithm for classification. It supports both binary and multiclass labels, as well as both continuous and categorical @@ -599,8 +597,6 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred @inherit_doc class DecisionTreeClassificationModel(DecisionTreeModel, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by DecisionTreeClassifier. .. versionadded:: 1.4.0 @@ -634,8 +630,6 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred RandomForestParams, TreeClassifierParams, HasCheckpointInterval, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - `Random Forest <http://en.wikipedia.org/wiki/Random_forest>`_ learning algorithm for classification. It supports both binary and multiclass labels, as well as both continuous and categorical @@ -730,8 +724,6 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred class RandomForestClassificationModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by RandomForestClassifier. .. versionadded:: 1.4.0 @@ -764,8 +756,6 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol GBTParams, HasCheckpointInterval, HasStepSize, HasSeed, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - `Gradient-Boosted Trees (GBTs) <http://en.wikipedia.org/wiki/Gradient_boosting>`_ learning algorithm for classification. It supports binary labels, as well as both continuous and categorical features. @@ -885,8 +875,6 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol class GBTClassificationModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by GBTClassifier. .. versionadded:: 1.4.0 @@ -918,8 +906,6 @@ class GBTClassificationModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable) class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasProbabilityCol, HasRawPredictionCol, HasThresholds, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Naive Bayes Classifiers. It supports both Multinomial and Bernoulli NB. `Multinomial NB <http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html>`_ @@ -1043,8 +1029,6 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H class NaiveBayesModel(JavaModel, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by NaiveBayes. .. versionadded:: 1.5.0 diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index bbbb94f9a0..2881380152 100755 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -60,8 +60,6 @@ __all__ = ['Binarizer', @inherit_doc class Binarizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Binarize a column of continuous features given a threshold. >>> df = spark.createDataFrame([(0.5,)], ["values"]) @@ -125,8 +123,6 @@ class Binarizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, Java @inherit_doc class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Maps a column of continuous features to a column of feature buckets. >>> df = spark.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"]) @@ -200,8 +196,6 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, Jav @inherit_doc class CountVectorizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Extracts a vocabulary from document collections and generates a :py:attr:`CountVectorizerModel`. >>> df = spark.createDataFrame( @@ -348,8 +342,6 @@ class CountVectorizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, class CountVectorizerModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`CountVectorizer`. .. versionadded:: 1.6.0 @@ -367,8 +359,6 @@ class CountVectorizerModel(JavaModel, JavaMLReadable, JavaMLWritable): @inherit_doc class DCT(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A feature transformer that takes the 1D discrete cosine transform of a real vector. No zero padding is performed on the input vector. It returns a real vector of the same length representing the DCT. @@ -439,8 +429,6 @@ class DCT(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWrit class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided "weight" vector. In other words, it scales each column of the dataset by a scalar multiplier. @@ -505,8 +493,6 @@ class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReada class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Maps a sequence of terms to their term frequencies using the hashing trick. Currently we use Austin Appleby's MurmurHash 3 algorithm (MurmurHash3_x86_32) to calculate the hash code value for the term object. @@ -576,8 +562,6 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures, Java @inherit_doc class IDF(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Compute the Inverse Document Frequency (IDF) given a collection of documents. >>> from pyspark.ml.linalg import DenseVector @@ -653,8 +637,6 @@ class IDF(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritab class IDFModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`IDF`. .. versionadded:: 1.4.0 @@ -752,8 +734,6 @@ class MaxAbsScalerModel(JavaModel, JavaMLReadable, JavaMLWritable): @inherit_doc class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling. The rescaled value for feature E is calculated as, @@ -859,8 +839,6 @@ class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, Jav class MinMaxScalerModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`MinMaxScaler`. .. versionadded:: 1.6.0 @@ -887,8 +865,6 @@ class MinMaxScalerModel(JavaModel, JavaMLReadable, JavaMLWritable): @ignore_unicode_prefix class NGram(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A feature transformer that converts the input array of strings into an array of n-grams. Null values in the input array are ignored. It returns an array of n-grams where each n-gram is represented by a space-separated string of @@ -965,8 +941,6 @@ class NGram(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWr @inherit_doc class Normalizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Normalize a vector to have unit norm using the given p-norm. >>> from pyspark.ml.linalg import Vectors @@ -1031,8 +1005,6 @@ class Normalizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, Jav @inherit_doc class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index. @@ -1114,8 +1086,6 @@ class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Perform feature expansion in a polynomial space. As said in `wikipedia of Polynomial Expansion <http://en.wikipedia.org/wiki/Polynomial_expansion>`_, "In mathematics, an expansion of a product of sums expresses it as a sum of products by using the fact that @@ -1287,8 +1257,6 @@ class QuantileDiscretizer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadab @ignore_unicode_prefix class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A regex based tokenizer that extracts tokens either by using the provided regex pattern (in Java dialect) to split the text (default) or repeatedly matching the regex (if gaps is false). @@ -1418,8 +1386,6 @@ class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, @inherit_doc class SQLTransformer(JavaTransformer, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Implements the transforms which are defined by SQL statement. Currently we only support SQL syntax like 'SELECT ... FROM __THIS__' where '__THIS__' represents the underlying table of the input dataset. @@ -1479,8 +1445,6 @@ class SQLTransformer(JavaTransformer, JavaMLReadable, JavaMLWritable): @inherit_doc class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set. @@ -1576,8 +1540,6 @@ class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, J class StandardScalerModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`StandardScaler`. .. versionadded:: 1.4.0 @@ -1604,8 +1566,6 @@ class StandardScalerModel(JavaModel, JavaMLReadable, JavaMLWritable): class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol, HasHandleInvalid, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A label indexer that maps a string column of labels to an ML column of label indices. If the input column is numeric, we cast it to string and index the string values. The indices are in [0, numLabels), ordered by label frequencies. @@ -1668,8 +1628,6 @@ class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol, HasHandleInvalid, class StringIndexerModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`StringIndexer`. .. versionadded:: 1.4.0 @@ -1687,8 +1645,6 @@ class StringIndexerModel(JavaModel, JavaMLReadable, JavaMLWritable): @inherit_doc class IndexToString(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A :py:class:`Transformer` that maps a column of indices back to a new column of corresponding string values. The index-string mapping is either from the ML attributes of the input column, @@ -1741,8 +1697,6 @@ class IndexToString(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A feature transformer that filters out stop words from input. Note: null values from input array are preserved unless adding null to stopWords explicitly. @@ -1833,8 +1787,6 @@ class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadabl @ignore_unicode_prefix class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A tokenizer that converts the input string to lowercase and then splits it by white spaces. @@ -1888,8 +1840,6 @@ class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, Java @inherit_doc class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - A feature transformer that merges multiple columns into a vector column. >>> df = spark.createDataFrame([(1, 0, 3)], ["a", "b", "c"]) @@ -1934,8 +1884,6 @@ class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol, JavaMLReadabl @inherit_doc class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Class for indexing categorical feature columns in a dataset of `Vector`. This has 2 usage modes: @@ -2050,8 +1998,6 @@ class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, Ja class VectorIndexerModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`VectorIndexer`. Transform categorical features to use 0-based indices instead of their original values. @@ -2089,8 +2035,6 @@ class VectorIndexerModel(JavaModel, JavaMLReadable, JavaMLWritable): @inherit_doc class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - This class takes a feature vector and outputs a new feature vector with a subarray of the original features. @@ -2183,8 +2127,6 @@ class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, J class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further natural language processing or machine learning process. @@ -2352,8 +2294,6 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has class Word2VecModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`Word2Vec`. .. versionadded:: 1.4.0 @@ -2383,8 +2323,6 @@ class Word2VecModel(JavaModel, JavaMLReadable, JavaMLWritable): @inherit_doc class PCA(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - PCA trains a model to project vectors to a lower dimensional space of the top :py:attr:`k` principal components. @@ -2458,8 +2396,6 @@ class PCA(JavaEstimator, HasInputCol, HasOutputCol, JavaMLReadable, JavaMLWritab class PCAModel(JavaModel, JavaMLReadable, JavaMLWritable): """ - .. note:: Experimental - Model fitted by :py:class:`PCA`. Transforms vectors to a lower dimensional space. .. versionadded:: 1.5.0 diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 8de9ad8531..d88dc75353 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -41,8 +41,6 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept, HasStandardization, HasSolver, HasWeightCol, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Linear regression. The learning objective is to minimize the squared error, with regularization. @@ -130,8 +128,6 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction class LinearRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by :class:`LinearRegression`. .. versionadded:: 1.4.0 @@ -411,8 +407,6 @@ class LinearRegressionTrainingSummary(LinearRegressionSummary): class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Currently implemented using parallelized pool adjacent violators algorithm. Only univariate (single feature) algorithm supported. @@ -439,6 +433,8 @@ class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti True >>> model.predictions == model2.predictions True + + .. versionadded:: 1.6.0 """ isotonic = \ @@ -505,13 +501,13 @@ class IsotonicRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti class IsotonicRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by :class:`IsotonicRegression`. + + .. versionadded:: 1.6.0 """ @property - @since("2.0.0") + @since("1.6.0") def boundaries(self): """ Boundaries in increasing order for which predictions are known. @@ -519,7 +515,7 @@ class IsotonicRegressionModel(JavaModel, JavaMLWritable, JavaMLReadable): return self._call_java("boundaries") @property - @since("2.0.0") + @since("1.6.0") def predictions(self): """ Predictions associated with the boundaries at the same index, monotone because of isotonic @@ -642,8 +638,6 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi DecisionTreeParams, TreeRegressorParams, HasCheckpointInterval, HasSeed, JavaMLWritable, JavaMLReadable, HasVarianceCol): """ - .. note:: Experimental - `Decision tree <http://en.wikipedia.org/wiki/Decision_tree_learning>`_ learning algorithm for regression. It supports both continuous and categorical features. @@ -727,8 +721,6 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi @inherit_doc class DecisionTreeModel(JavaModel): """ - .. note:: Experimental - Abstraction for Decision Tree models. .. versionadded:: 1.5.0 @@ -759,11 +751,9 @@ class DecisionTreeModel(JavaModel): @inherit_doc class TreeEnsembleModels(JavaModel): """ - .. note:: Experimental + (private abstraction) Represents a tree ensemble model. - - .. versionadded:: 1.5.0 """ @property @@ -803,8 +793,6 @@ class TreeEnsembleModels(JavaModel): @inherit_doc class DecisionTreeRegressionModel(DecisionTreeModel, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by :class:`DecisionTreeRegressor`. .. versionadded:: 1.4.0 @@ -837,8 +825,6 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi RandomForestParams, TreeRegressorParams, HasCheckpointInterval, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - `Random Forest <http://en.wikipedia.org/wiki/Random_forest>`_ learning algorithm for regression. It supports both continuous and categorical features. @@ -925,8 +911,6 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi class RandomForestRegressionModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by :class:`RandomForestRegressor`. .. versionadded:: 1.4.0 @@ -959,8 +943,6 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, GBTParams, HasCheckpointInterval, HasStepSize, HasSeed, JavaMLWritable, JavaMLReadable, TreeRegressorParams): """ - .. note:: Experimental - `Gradient-Boosted Trees (GBTs) <http://en.wikipedia.org/wiki/Gradient_boosting>`_ learning algorithm for regression. It supports both continuous and categorical features. @@ -1067,8 +1049,6 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, class GBTRegressionModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable): """ - .. note:: Experimental - Model fitted by :class:`GBTRegressor`. .. versionadded:: 1.4.0 diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index f857c5e8c8..298314d46c 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -33,8 +33,6 @@ __all__ = ['ParamGridBuilder', 'CrossValidator', 'CrossValidatorModel', 'TrainVa class ParamGridBuilder(object): r""" - .. note:: Experimental - Builder for a param grid used in grid search-based model selection. >>> from pyspark.ml.classification import LogisticRegression @@ -145,8 +143,6 @@ class ValidatorParams(HasSeed): class CrossValidator(Estimator, ValidatorParams): """ - .. note:: Experimental - K-fold cross validation. >>> from pyspark.ml.classification import LogisticRegression @@ -264,8 +260,6 @@ class CrossValidator(Estimator, ValidatorParams): class CrossValidatorModel(Model, ValidatorParams): """ - .. note:: Experimental - Model from k-fold cross validation. .. versionadded:: 1.4.0 |