TableVertical Class
- java.
lang. Object - com.
azure. resourcemanager. machinelearning. models. TableVertical
- com.
Implements
public class TableVertical
implements JsonSerializable<TableVertical>
Abstract class for AutoML tasks that use table dataset as input - such as Classification/Regression/Forecasting.
Constructor Summary
| Constructor | Description |
|---|---|
| TableVertical() |
Creates an instance of Table |
Method Summary
| Modifier and Type | Method and Description |
|---|---|
| List<String> |
cvSplitColumnNames()
Get the cv |
|
Table |
featurizationSettings()
Get the featurization |
|
static
Table |
fromJson(JsonReader jsonReader)
Reads an instance of Table |
|
Table |
limitSettings()
Get the limit |
|
NCross |
nCrossValidations()
Get the n |
|
MLTable |
testData()
Get the test |
| Double |
testDataSize()
Get the test |
|
Json |
toJson(JsonWriter jsonWriter) |
| void |
validate()
Validates the instance. |
|
MLTable |
validationData()
Get the validation |
| Double |
validationDataSize()
Get the validation |
| String |
weightColumnName()
Get the weight |
|
Table |
withCvSplitColumnNames(List<String> cvSplitColumnNames)
Set the cv |
|
Table |
withFeaturizationSettings(TableVerticalFeaturizationSettings featurizationSettings)
Set the featurization |
|
Table |
withLimitSettings(TableVerticalLimitSettings limitSettings)
Set the limit |
|
Table |
withNCrossValidations(NCrossValidations nCrossValidations)
Set the n |
|
Table |
withTestData(MLTableJobInput testData)
Set the test |
|
Table |
withTestDataSize(Double testDataSize)
Set the test |
|
Table |
withValidationData(MLTableJobInput validationData)
Set the validation |
|
Table |
withValidationDataSize(Double validationDataSize)
Set the validation |
|
Table |
withWeightColumnName(String weightColumnName)
Set the weight |
Methods inherited from java.lang.Object
Constructor Details
TableVertical
public TableVertical()
Creates an instance of TableVertical class.
Method Details
cvSplitColumnNames
public List<String> cvSplitColumnNames()
Get the cvSplitColumnNames property: Columns to use for CVSplit data.
Returns:
featurizationSettings
public TableVerticalFeaturizationSettings featurizationSettings()
Get the featurizationSettings property: Featurization inputs needed for AutoML job.
Returns:
fromJson
public static TableVertical fromJson(JsonReader jsonReader)
Reads an instance of TableVertical from the JsonReader.
Parameters:
Returns:
Throws:
limitSettings
public TableVerticalLimitSettings limitSettings()
Get the limitSettings property: Execution constraints for AutoMLJob.
Returns:
nCrossValidations
public NCrossValidations nCrossValidations()
Get the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
Returns:
testData
public MLTableJobInput testData()
Get the testData property: Test data input.
Returns:
testDataSize
public Double testDataSize()
Get the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
Returns:
toJson
validate
public void validate()
Validates the instance.
validationData
public MLTableJobInput validationData()
Get the validationData property: Validation data inputs.
Returns:
validationDataSize
public Double validationDataSize()
Get the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
Returns:
weightColumnName
public String weightColumnName()
Get the weightColumnName property: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
Returns:
withCvSplitColumnNames
public TableVertical withCvSplitColumnNames(List<String> cvSplitColumnNames)
Set the cvSplitColumnNames property: Columns to use for CVSplit data.
Parameters:
Returns:
withFeaturizationSettings
public TableVertical withFeaturizationSettings(TableVerticalFeaturizationSettings featurizationSettings)
Set the featurizationSettings property: Featurization inputs needed for AutoML job.
Parameters:
Returns:
withLimitSettings
public TableVertical withLimitSettings(TableVerticalLimitSettings limitSettings)
Set the limitSettings property: Execution constraints for AutoMLJob.
Parameters:
Returns:
withNCrossValidations
public TableVertical withNCrossValidations(NCrossValidations nCrossValidations)
Set the nCrossValidations property: Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
Parameters:
Returns:
withTestData
public TableVertical withTestData(MLTableJobInput testData)
Set the testData property: Test data input.
Parameters:
Returns:
withTestDataSize
public TableVertical withTestDataSize(Double testDataSize)
Set the testDataSize property: The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
Parameters:
Returns:
withValidationData
public TableVertical withValidationData(MLTableJobInput validationData)
Set the validationData property: Validation data inputs.
Parameters:
Returns:
withValidationDataSize
public TableVertical withValidationDataSize(Double validationDataSize)
Set the validationDataSize property: The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
Parameters:
Returns:
withWeightColumnName
public TableVertical withWeightColumnName(String weightColumnName)
Set the weightColumnName property: The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.
Parameters:
Returns:
