Regression interface
Regression task in AutoML Table vertical.
- Extends
Properties
primary |
Primary metric for regression task. |
task |
Polymorphic discriminator, which specifies the different types this object can be |
training |
Inputs for training phase for an AutoML Job. |
Inherited Properties
cv |
Columns to use for CVSplit data. |
featurization |
Featurization inputs needed for AutoML job. |
limit |
Execution constraints for AutoMLJob. |
log |
Log verbosity for the job. |
n |
Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
target |
Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
test |
Test data input. |
test |
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. |
training |
[Required] Training data input. |
validation |
Validation data inputs. |
validation |
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. |
weight |
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. |
Property Details
primaryMetric
Primary metric for regression task.
primaryMetric?: string
Property Value
string
taskType
Polymorphic discriminator, which specifies the different types this object can be
taskType: "Regression"
Property Value
"Regression"
trainingSettings
Inputs for training phase for an AutoML Job.
trainingSettings?: RegressionTrainingSettings
Property Value
Inherited Property Details
cvSplitColumnNames
Columns to use for CVSplit data.
cvSplitColumnNames?: string[]
Property Value
string[]
Inherited From TableVertical.cvSplitColumnNames
featurizationSettings
Featurization inputs needed for AutoML job.
featurizationSettings?: TableVerticalFeaturizationSettings
Property Value
Inherited From TableVertical.featurizationSettings
limitSettings
Execution constraints for AutoMLJob.
limitSettings?: TableVerticalLimitSettings
Property Value
Inherited From TableVertical.limitSettings
logVerbosity
Log verbosity for the job.
logVerbosity?: string
Property Value
string
Inherited From AutoMLVertical.logVerbosity
nCrossValidations
Number of cross validation folds to be applied on training dataset when validation dataset is not provided.
nCrossValidations?: NCrossValidationsUnion
Property Value
Inherited From TableVertical.nCrossValidations
targetColumnName
Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
targetColumnName?: string
Property Value
string
Inherited From AutoMLVertical.targetColumnName
testData
testDataSize
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.
testDataSize?: number
Property Value
number
Inherited From TableVertical.testDataSize
trainingData
[Required] Training data input.
trainingData: MLTableJobInput
Property Value
Inherited From AutoMLVertical.trainingData
validationData
Validation data inputs.
validationData?: MLTableJobInput
Property Value
Inherited From TableVertical.validationData
validationDataSize
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.
validationDataSize?: number
Property Value
number
Inherited From TableVertical.validationDataSize
weightColumnName
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.
weightColumnName?: string
Property Value
string
Inherited From TableVertical.weightColumnName