NaiveBayesModel

Instance Constructors

new NaiveBayesModel(numFeatures: Int, numClasses: Int, pi: Vector, theta: Matrix, modelType: ModelType)

numFeatures
number of features in feature vector
numClasses
number of labels or labels to classify predictions into
pi
log of class priors
theta
log of class conditional probabilities

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
def apply(features: Vector): Double

Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.
Alias for ml.combust.mleap.core.classification.ClassificationModel#predict.
features
feature vector
returns
prediction

Definition Classes
ClassificationModel
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

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

Definition Classes
AnyRef
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

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

Definition Classes
Any
val modelType: ModelType
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val numClasses: Int

number of labels or labels to classify predictions into
number of labels or labels to classify predictions into

Definition Classes
NaiveBayesModel → ProbabilisticClassificationModel
val numFeatures: Int

number of features in feature vector
val pi: Vector

log of class priors
def predict(features: Vector): Double

Predict class based on feature vector.
Predict class based on feature vector.
features
feature vector
returns
predicted class or probability

Definition Classes
ProbabilisticClassificationModel → ClassificationModel
def predictProbabilities(features: Vector): Vector

Definition Classes
ProbabilisticClassificationModel
def predictRaw(raw: Vector): Vector

Definition Classes
NaiveBayesModel → ProbabilisticClassificationModel
def predictWithProbability(features: Vector): (Double, Double)

Definition Classes
ProbabilisticClassificationModel
def probabilityToPrediction(probability: Vector): Double

Definition Classes
ProbabilisticClassificationModel
def rawToPrediction(raw: Vector): Double

Definition Classes
ProbabilisticClassificationModel
def rawToProbability(raw: Vector): Vector

Definition Classes
ProbabilisticClassificationModel
def rawToProbabilityInPlace(raw: Vector): Vector

Definition Classes
NaiveBayesModel → ProbabilisticClassificationModel
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val theta: Matrix

log of class conditional probabilities
def thresholds: Option[Array[Double]]

Definition Classes
ProbabilisticClassificationModel
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object NaiveBayesModel | package classification

case class NaiveBayesModel(numFeatures: Int, numClasses: Int, pi: Vector, theta: Matrix, modelType: ModelType) extends ProbabilisticClassificationModel with Serializable with Product

Instance Constructors

new NaiveBayesModel(numFeatures: Int, numClasses: Int, pi: Vector, theta: Matrix, modelType: ModelType)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def apply(features: Vector): Double

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def finalize(): Unit

final def getClass(): Class[_]

final def isInstanceOf[T0]: Boolean

val modelType: ModelType

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val numClasses: Int

val numFeatures: Int

val pi: Vector

def predict(features: Vector): Double

def predictProbabilities(features: Vector): Vector

def predictRaw(raw: Vector): Vector

def predictWithProbability(features: Vector): (Double, Double)

def probabilityToPrediction(probability: Vector): Double

def rawToPrediction(raw: Vector): Double

def rawToProbability(raw: Vector): Vector

def rawToProbabilityInPlace(raw: Vector): Vector

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

val theta: Matrix

def thresholds: Option[Array[Double]]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Product

Inherited from Equals

Inherited from Serializable

Inherited from Serializable

Inherited from ProbabilisticClassificationModel

Inherited from ClassificationModel

Inherited from AnyRef

Inherited from Any

Ungrouped