Package org.apache.spark.ml.feature
Class StandardScaler
- All Implemented Interfaces:
- Serializable,- org.apache.spark.internal.Logging,- StandardScalerParams,- Params,- HasInputCol,- HasOutputCol,- DefaultParamsWritable,- Identifiable,- MLWritable
public class StandardScaler
extends Estimator<StandardScalerModel>
implements StandardScalerParams, DefaultParamsWritable
Standardizes features by removing the mean and scaling to unit variance using column summary
 statistics on the samples in the training set.
 
The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.
- See Also:
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Nested Class SummaryNested classes/interfaces inherited from interface org.apache.spark.internal.Loggingorg.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
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Constructor SummaryConstructors
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Method SummaryModifier and TypeMethodDescriptionCreates a copy of this instance with the same UID and some extra params.Fits a model to the input data.inputCol()Param for input column name.static StandardScalerParam for output column name.static MLReader<T>read()setInputCol(String value) setOutputCol(String value) setWithMean(boolean value) setWithStd(boolean value) transformSchema(StructType schema) Check transform validity and derive the output schema from the input schema.uid()An immutable unique ID for the object and its derivatives.withMean()Whether to center the data with mean before scaling.withStd()Whether to scale the data to unit standard deviation.Methods inherited from class org.apache.spark.ml.PipelineStageparamsMethods inherited from class java.lang.Objectequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitMethods inherited from interface org.apache.spark.ml.util.DefaultParamsWritablewriteMethods inherited from interface org.apache.spark.ml.param.shared.HasInputColgetInputColMethods inherited from interface org.apache.spark.ml.param.shared.HasOutputColgetOutputColMethods inherited from interface org.apache.spark.ml.util.IdentifiabletoStringMethods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logBasedOnLevel, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, MDC, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContextMethods inherited from interface org.apache.spark.ml.util.MLWritablesaveMethods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, estimateMatadataSize, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnMethods inherited from interface org.apache.spark.ml.feature.StandardScalerParamsgetWithMean, getWithStd, validateAndTransformSchema
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Constructor Details- 
StandardScaler
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StandardScalerpublic StandardScaler()
 
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Method Details- 
load
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read
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withMeanDescription copied from interface:StandardScalerParamsWhether to center the data with mean before scaling. It will build a dense output, so take care when applying to sparse input. Default: false- Specified by:
- withMeanin interface- StandardScalerParams
- Returns:
- (undocumented)
 
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withStdDescription copied from interface:StandardScalerParamsWhether to scale the data to unit standard deviation. Default: true- Specified by:
- withStdin interface- StandardScalerParams
- Returns:
- (undocumented)
 
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outputColDescription copied from interface:HasOutputColParam for output column name.- Specified by:
- outputColin interface- HasOutputCol
- Returns:
- (undocumented)
 
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inputColDescription copied from interface:HasInputColParam for input column name.- Specified by:
- inputColin interface- HasInputCol
- Returns:
- (undocumented)
 
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uidDescription copied from interface:IdentifiableAn immutable unique ID for the object and its derivatives.- Specified by:
- uidin interface- Identifiable
- Returns:
- (undocumented)
 
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setInputCol
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setOutputCol
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setWithMean
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setWithStd
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fitDescription copied from class:EstimatorFits a model to the input data.- Specified by:
- fitin class- Estimator<StandardScalerModel>
- Parameters:
- dataset- (undocumented)
- Returns:
- (undocumented)
 
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transformSchemaDescription copied from class:PipelineStageCheck transform validity and derive the output schema from the input schema.We check validity for interactions between parameters during transformSchemaand raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate().Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks. - Specified by:
- transformSchemain class- PipelineStage
- Parameters:
- schema- (undocumented)
- Returns:
- (undocumented)
 
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copyDescription copied from interface:ParamsCreates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. SeedefaultCopy().- Specified by:
- copyin interface- Params
- Specified by:
- copyin class- Estimator<StandardScalerModel>
- Parameters:
- extra- (undocumented)
- Returns:
- (undocumented)
 
 
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