public class StandardScalerModel extends Model<StandardScalerModel> implements StandardScalerParams, MLWritable
StandardScaler.
param: std Standard deviation of the StandardScalerModel param: mean Mean of the StandardScalerModel
| Modifier and Type | Method and Description |
|---|---|
StandardScalerModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
inputCol()
Param for input column name.
|
static StandardScalerModel |
load(String path) |
Vector |
mean() |
Param<String> |
outputCol()
Param for output column name.
|
static MLReader<StandardScalerModel> |
read() |
StandardScalerModel |
setInputCol(String value) |
StandardScalerModel |
setOutputCol(String value) |
Vector |
std() |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
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.
|
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transformparamsgetWithMean, getWithStd, validateAndTransformSchemagetInputColgetOutputColclear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnsave$init$, 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 static MLReader<StandardScalerModel> read()
public static StandardScalerModel load(String path)
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 Vector std()
public Vector mean()
public StandardScalerModel setInputCol(String value)
public StandardScalerModel setOutputCol(String value)
public Dataset<Row> transform(Dataset<?> dataset)
Transformertransform in class Transformerdataset - (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 StandardScalerModel copy(ParamMap extra)
ParamsdefaultCopy().copy in interface Paramscopy in class Model<StandardScalerModel>extra - (undocumented)public MLWriter write()
MLWritableMLWriter instance for this ML instance.write in interface MLWritablepublic String toString()
toString in interface IdentifiabletoString in class Object