class TrainClassifier extends Estimator[TrainedClassifierModel] with AutoTrainer[TrainedClassifierModel] with SynapseMLLogging
Trains a classification model. Featurizes the given data into a vector of doubles.
Note the behavior of the reindex and labels parameters, the parameters interact as:
reindex -> false labels -> false (Empty) Assume all double values, don't use metadata, assume natural ordering
reindex -> true labels -> false (Empty) Index, use natural ordering of string indexer
reindex -> false labels -> true (Specified) Assume user knows indexing, apply label values. Currently only string type supported.
reindex -> true labels -> true (Specified) Validate labels matches column type, try to recast to label type, reindex label column
The currently supported classifiers are: Logistic Regression Classifier Decision Tree Classifier Random Forest Classifier Gradient Boosted Trees Classifier Naive Bayes Classifier Multilayer Perceptron Classifier In addition to any generic learner that inherits from Predictor.
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clear(param: Param[_]): TrainClassifier.this.type
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copy(extra: ParamMap): Estimator[TrainedClassifierModel]
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featuresCol: Param[String]
The name of the features column
The name of the features column
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def
fit(dataset: Dataset[_]): TrainedClassifierModel
Fits the classification model.
Fits the classification model.
- dataset
The input dataset to train.
- returns
The trained classification model.
- Definition Classes
- TrainClassifier → Estimator
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def
fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[TrainedClassifierModel]
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def
fit(dataset: Dataset[_], paramMap: ParamMap): TrainedClassifierModel
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def
fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedClassifierModel
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getFeaturesCol: String
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getInputCols: Array[String]
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getLabelCol: String
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getModel: Estimator[_ <: Model[_]]
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getNumFeatures: Int
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def
getPayload(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], exception: Option[Exception]): Map[String, String]
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def
initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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def
initializeLogIfNecessary(isInterpreter: Boolean): Unit
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val
inputCols: StringArrayParam
The names of the inputColumns
The names of the inputColumns
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isTraceEnabled(): Boolean
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val
labelCol: Param[String]
The name of the label column
The name of the label column
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val
labels: StringArrayParam
Specifies the labels metadata on the column.
Specifies the labels metadata on the column. See class documentation for how this parameter interacts with reindex labels parameter.
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def
log: Logger
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logBase(info: Map[String, String], featureName: Option[String]): Unit
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logBase(methodName: String, numCols: Option[Int], executionSeconds: Option[Double], featureName: Option[String]): Unit
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val
model: EstimatorParam
Model to run.
Model to run. See doc on derived classes.
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def
modelDoc: String
Doc for model to run.
Doc for model to run.
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- TrainClassifier → AutoTrainer
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val
numFeatures: IntParam
Number of features to hash to
Number of features to hash to
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params: Array[Param[_]]
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rSetterLines: String
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val
reindexLabel: BooleanParam
Specifies whether to reindex the given label column.
Specifies whether to reindex the given label column. See class documentation for how this parameter interacts with specified labels.
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def
save(path: String): Unit
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final
def
set(paramPair: ParamPair[_]): TrainClassifier.this.type
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final
def
set(param: String, value: Any): TrainClassifier.this.type
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def
set[T](param: Param[T], value: T): TrainClassifier.this.type
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def
setDefault(paramPairs: ParamPair[_]*): TrainClassifier.this.type
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def
setDefault[T](param: Param[T], value: T): TrainClassifier.this.type
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def
setFeaturesCol(value: String): TrainClassifier.this.type
- Definition Classes
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def
setInputCols(value: Array[String]): TrainClassifier.this.type
- Definition Classes
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def
setLabelCol(value: String): TrainClassifier.this.type
- Definition Classes
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- def setLabels(value: Array[String]): TrainClassifier.this.type
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def
setModel(value: Estimator[_ <: Model[_]]): TrainClassifier.this.type
- Definition Classes
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def
setNumFeatures(value: Int): TrainClassifier.this.type
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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val
thisStage: Params
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def
toString(): String
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def
transformSchema(schema: StructType): StructType
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def
transformSchema(schema: StructType, logging: Boolean): StructType
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val
uid: String
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- TrainClassifier → SynapseMLLogging → Identifiable
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final
def
wait(): Unit
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def
wait(arg0: Long, arg1: Int): Unit
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def
wait(arg0: Long): Unit
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def
write: MLWriter
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