object GBMSupport extends GBMSupport
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def
GBMModel[T <: Frame](train: T, test: T, response: String, modelId: String = "model", ntrees: Int = 50, depth: Int = 6, family: DistributionFamily = DistributionFamily.AUTO): GBMModel
Create Gradient Boosting Model for the basic usage.
Create Gradient Boosting Model for the basic usage. This method exposes the basic configuration of the model. If you need to specify some arguments which are not exposed, please use the DeepLearning model via Sparkling Water pipelines API or using the raw java API
- T
H2O Frame Type
- train
frame to train
- test
test frame
- response
response column
- modelId
name of the model
- ntrees
number of trees
- depth
depth
- family
distribution family
- returns
Gradient Boosting Model
- Definition Classes
- GBMSupport
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- @DeprecatedMethod( "ai.h2o.sparkling.ml.algos.H2OGBM" , "3.32" )
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logName: String
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Inherited from GBMSupport
Value Members
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def
GBMModel[T <: Frame](train: T, test: T, response: String, modelId: String = "model", ntrees: Int = 50, depth: Int = 6, family: DistributionFamily = DistributionFamily.AUTO): GBMModel
Create Gradient Boosting Model for the basic usage.
Create Gradient Boosting Model for the basic usage. This method exposes the basic configuration of the model. If you need to specify some arguments which are not exposed, please use the DeepLearning model via Sparkling Water pipelines API or using the raw java API
- T
H2O Frame Type
- train
frame to train
- test
test frame
- response
response column
- modelId
name of the model
- ntrees
number of trees
- depth
depth
- family
distribution family
- returns
Gradient Boosting Model
- Definition Classes
- GBMSupport
- Annotations
- @DeprecatedMethod( "ai.h2o.sparkling.ml.algos.H2OGBM" , "3.32" )
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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initializeLogIfNecessary(isInterpreter: Boolean): Unit
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isTraceEnabled(): Boolean
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log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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logInfo(msg: ⇒ String): Unit
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logName: String
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def
GBMModel[T <: Frame](train: T, test: T, response: String, modelId: String = "model", ntrees: Int = 50, depth: Int = 6, family: DistributionFamily = DistributionFamily.AUTO): GBMModel
Create Gradient Boosting Model for the basic usage.
Create Gradient Boosting Model for the basic usage. This method exposes the basic configuration of the model. If you need to specify some arguments which are not exposed, please use the DeepLearning model via Sparkling Water pipelines API or using the raw java API
- T
H2O Frame Type
- train
frame to train
- test
test frame
- response
response column
- modelId
name of the model
- ntrees
number of trees
- depth
depth
- family
distribution family
- returns
Gradient Boosting Model
- Definition Classes
- GBMSupport
- Annotations
- @DeprecatedMethod( "ai.h2o.sparkling.ml.algos.H2OGBM" , "3.32" )
-
final
def
asInstanceOf[T0]: T0
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def
clone(): AnyRef
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equals(arg0: Any): Boolean
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def
finalize(): Unit
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getClass(): Class[_]
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def
hashCode(): Int
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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final
def
isInstanceOf[T0]: Boolean
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def
isTraceEnabled(): Boolean
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def
log: Logger
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def
logDebug(msg: ⇒ String, throwable: Throwable): Unit
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def
logDebug(msg: ⇒ String): Unit
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def
logError(msg: ⇒ String, throwable: Throwable): Unit
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logError(msg: ⇒ String): Unit
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logInfo(msg: ⇒ String, throwable: Throwable): Unit
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def
logInfo(msg: ⇒ String): Unit
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def
logName: String
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