public class StandardScaler extends Estimator<StandardScalerModel> implements StandardScalerParams, DefaultParamsWritable
The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.
| Constructor and Description |
|---|
StandardScaler() |
StandardScaler(String uid) |
| Modifier and Type | Method and Description |
|---|---|
StandardScaler |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
StandardScalerModel |
fit(Dataset<?> dataset)
Fits a model to the input data.
|
Param<String> |
inputCol()
Param for input column name.
|
static StandardScaler |
load(String path) |
Param<String> |
outputCol()
Param for output column name.
|
static MLReader<T> |
read() |
StandardScaler |
setInputCol(String value) |
StandardScaler |
setOutputCol(String value) |
StandardScaler |
setWithMean(boolean value) |
StandardScaler |
setWithStd(boolean value) |
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
BooleanParam |
withMean()
Whether to center the data with mean before scaling.
|
BooleanParam |
withStd()
Whether to scale the data to unit standard deviation.
|
paramsequals, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitgetWithMean, getWithStd, validateAndTransformSchemagetInputColgetOutputColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwntoStringwritesave$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitializepublic StandardScaler(String uid)
public StandardScaler()
public static StandardScaler load(String path)
public static MLReader<T> read()
public BooleanParam withMean()
StandardScalerParamswithMean in interface StandardScalerParamspublic BooleanParam withStd()
StandardScalerParamswithStd in interface StandardScalerParamspublic final Param<String> outputCol()
HasOutputColoutputCol in interface HasOutputColpublic final Param<String> inputCol()
HasInputColinputCol in interface HasInputColpublic String uid()
Identifiableuid in interface Identifiablepublic StandardScaler setInputCol(String value)
public StandardScaler setOutputCol(String value)
public StandardScaler setWithMean(boolean value)
public StandardScaler setWithStd(boolean value)
public StandardScalerModel fit(Dataset<?> dataset)
Estimatorfit in class Estimator<StandardScalerModel>dataset - (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
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 PipelineStageschema - (undocumented)public StandardScaler copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Estimator<StandardScalerModel>extra - (undocumented)