Package org.apache.spark.ml.util
Interface DefaultParamsWritable
- All Superinterfaces:
- MLWritable
- All Known Implementing Classes:
- AFTSurvivalRegression,- ALS,- Binarizer,- BinaryClassificationEvaluator,- BisectingKMeans,- BucketedRandomProjectionLSH,- Bucketizer,- ChiSqSelector,- ClusteringEvaluator,- CountVectorizer,- DCT,- DecisionTreeClassifier,- DecisionTreeRegressor,- ElementwiseProduct,- FeatureHasher,- FMClassifier,- FMRegressor,- FPGrowth,- GaussianMixture,- GBTClassifier,- GBTRegressor,- GeneralizedLinearRegression,- HashingTF,- IDF,- Imputer,- IndexToString,- Interaction,- IsotonicRegression,- KMeans,- LDA,- LinearRegression,- LinearSVC,- LogisticRegression,- MaxAbsScaler,- MinHashLSH,- MinMaxScaler,- MulticlassClassificationEvaluator,- MultilabelClassificationEvaluator,- MultilayerPerceptronClassifier,- NaiveBayes,- NGram,- Normalizer,- OneHotEncoder,- PCA,- PolynomialExpansion,- PowerIterationClustering,- QuantileDiscretizer,- RandomForestClassifier,- RandomForestRegressor,- RankingEvaluator,- RegexTokenizer,- RegressionEvaluator,- RFormula,- RobustScaler,- SQLTransformer,- StandardScaler,- StopWordsRemover,- StringIndexer,- TargetEncoder,- Tokenizer,- UnivariateFeatureSelector,- VarianceThresholdSelector,- VectorAssembler,- VectorIndexer,- VectorSizeHint,- VectorSlicer,- Word2Vec
Helper trait for making simple 
Params types writable.  If a Params class stores
 all data as Param values, then extending this trait will provide
 a default implementation of writing saved instances of the class.
 This only handles simple Param types; e.g., it will not handle
 Dataset.
 - See Also:
- 
- DefaultParamsReadable, the counterpart to this trait
 
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Method SummaryMethods inherited from interface org.apache.spark.ml.util.MLWritablesave
- 
Method Details- 
writeMLWriter write()Description copied from interface:MLWritableReturns anMLWriterinstance for this ML instance.- Specified by:
- writein interface- MLWritable
- Returns:
- (undocumented)
 
 
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