NaiveBayesModel

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
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Annotations
@throws( ... )
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
def formatVersion: String

Current version of model save/load format.
Current version of model save/load format.

Attributes
protected
Definition Classes
NaiveBayesModel → Saveable
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val labels: Array[Double]

list of labels
list of labels

Annotations
@Since( "1.0.0" )
val modelType: String

The type of NB model to fit can be "multinomial" or "bernoulli"
The type of NB model to fit can be "multinomial" or "bernoulli"

Annotations
@Since( "1.4.0" )
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
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final def notifyAll(): Unit

Definition Classes
AnyRef
val pi: Array[Double]

log of class priors, whose dimension is C, number of labels
log of class priors, whose dimension is C, number of labels

Annotations
@Since( "0.9.0" )
def predict(testData: Vector): Double

Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
testData
array representing a single data point
returns
predicted category from the trained model

Definition Classes
NaiveBayesModel → ClassificationModel
Annotations
@Since( "1.0.0" )
def predict(testData: RDD[Vector]): RDD[Double]

Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
testData
RDD representing data points to be predicted
returns
an RDD[Double] where each entry contains the corresponding prediction

Definition Classes
NaiveBayesModel → ClassificationModel
Annotations
@Since( "1.0.0" )
def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

Predict values for examples stored in a JavaRDD.
Predict values for examples stored in a JavaRDD.
testData
JavaRDD representing data points to be predicted
returns
a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction

Definition Classes
ClassificationModel
Annotations
@Since( "1.0.0" )
def predictProbabilities(testData: Vector): Vector

Predict posterior class probabilities for a single data point using the model trained.
Predict posterior class probabilities for a single data point using the model trained.
testData
array representing a single data point
returns
predicted posterior class probabilities from the trained model, in the same order as class labels

Annotations
@Since( "1.5.0" )
def predictProbabilities(testData: RDD[Vector]): RDD[Vector]

Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
testData
RDD representing data points to be predicted
returns
an RDD[Vector] where each entry contains the predicted posterior class probabilities, in the same order as class labels

Annotations
@Since( "1.5.0" )
def save(sc: SparkContext, path: String): Unit

Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using Loader.load.
sc
Spark context used to save model data.
path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

Definition Classes
NaiveBayesModel → Saveable
Annotations
@Since( "1.3.0" )
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val theta: Array[Array[Double]]

log of class conditional probabilities, whose dimension is C-by-D, where D is number of features
log of class conditional probabilities, whose dimension is C-by-D, where D is number of features

Annotations
@Since( "0.9.0" )
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
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Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object NaiveBayesModel | package classification

class NaiveBayesModel extends ClassificationModel with Serializable with Saveable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def formatVersion: String

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val labels: Array[Double]

val modelType: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val pi: Array[Double]

def predict(testData: Vector): Double

def predict(testData: RDD[Vector]): RDD[Double]

def predict(testData: JavaRDD[Vector]): JavaRDD[Double]

def predictProbabilities(testData: Vector): Vector

def predictProbabilities(testData: RDD[Vector]): RDD[Vector]

def save(sc: SparkContext, path: String): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

val theta: Array[Array[Double]]

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Saveable

Inherited from ClassificationModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped