Serializable
, org.apache.spark.internal.Logging
, StandardScalerParams
, Params
, HasInputCol
, HasOutputCol
, DefaultParamsWritable
, Identifiable
, MLWritable
The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.
org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter
Constructors
Creates a copy of this instance with the same UID and some extra params.
Fits a model to the input data.
Param for input column name.
Param for output column name.
Check transform validity and derive the output schema from the input schema.
An immutable unique ID for the object and its derivatives.
Whether to center the data with mean before scaling.
Whether to scale the data to unit standard deviation.
Methods inherited from interface org.apache.spark.internal.LogginginitializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext
Methods inherited from interface org.apache.spark.ml.util.MLWritablesave
Methods inherited from interface org.apache.spark.ml.param.Paramsclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
public StandardScaler()
Whether to center the data with mean before scaling. It will build a dense output, so take care when applying to sparse input. Default: false
withMean
in interface StandardScalerParams
Whether to scale the data to unit standard deviation. Default: true
withStd
in interface StandardScalerParams
Param for output column name.
outputCol
in interface HasOutputCol
Param for input column name.
inputCol
in interface HasInputCol
An immutable unique ID for the object and its derivatives.
uid
in interface Identifiable
Fits a model to the input data.
fit
in class Estimator<StandardScalerModel>
dataset
- (undocumented)
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during transformSchema
and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate()
.
Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
transformSchema
in class PipelineStage
schema
- (undocumented)
Params
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy()
.
copy
in interface Params
copy
in class Estimator<StandardScalerModel>
extra
- (undocumented)
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