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 |
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StandardScaler() |
StandardScaler(String uid) |
Modifier and Type | Method and Description |
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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.
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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.
|
params
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
getWithMean, getWithStd, validateAndTransformSchema
getInputCol
getOutputCol
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
toString
write
save
$init$, 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_, uninitialize
public StandardScaler(String uid)
public StandardScaler()
public static StandardScaler load(String path)
public static MLReader<T> read()
public BooleanParam withMean()
StandardScalerParams
withMean
in interface StandardScalerParams
public BooleanParam withStd()
StandardScalerParams
withStd
in interface StandardScalerParams
public final Param<String> outputCol()
HasOutputCol
outputCol
in interface HasOutputCol
public final Param<String> inputCol()
HasInputCol
inputCol
in interface HasInputCol
public String uid()
Identifiable
uid
in interface Identifiable
public StandardScaler setInputCol(String value)
public StandardScaler setOutputCol(String value)
public StandardScaler setWithMean(boolean value)
public StandardScaler setWithStd(boolean value)
public StandardScalerModel fit(Dataset<?> dataset)
Estimator
fit
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 PipelineStage
schema
- (undocumented)public StandardScaler copy(ParamMap extra)
Params
defaultCopy()
.copy
in interface Params
copy
in class Estimator<StandardScalerModel>
extra
- (undocumented)