NaiveBayesClassifier

Instance Constructors

new NaiveBayesClassifier(json: JsValue)
new NaiveBayesClassifier(model: String = NaiveBayesClassifier.model, priors: List[Double] = NaiveBayesClassifier.priors, alpha: Double = NaiveBayesClassifier.alpha, degree: Int = NaiveBayesClassifier.degree)

model
The distribution function to assume for the feature distributions
priors
Prior probabilities to assume for the classes
alpha
Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing)
degree
Order of polynomial features to add to the instances (1 for no addition)

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 _predict(X: List[List[Double]]): List[Int]
def _train(X: List[List[Double]], y: List[Int]): Unit
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def diagnostics(): Map[String, List[(Double, Double)]]

Provides meta-information on the classifier
Provides meta-information on the classifier
returns
Map object of metric names and metric values

Definition Classes
Classifier
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def finalize(): Unit

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

Definition Classes
AnyRef → Any
def getLikeli(x: List[Double]): List[List[Double]]

Calculates the per class and per feature likelihood value of a given instance
def getProbabs(x: List[Double]): List[Double]

Calculates the probabilities of belonging to a class for a given instance
Calculates the probabilities of belonging to a class for a given instance
This bases on Bayes theorem: p(C | x) = p(x | C) * p(C) / const using the naive assumption, that p(x0, ..., xn | C) * p(C) = p(C) * p(x0 | C) * ... * p(xn | C). The constant factor is neglected.
def hashCode(): Int

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

Definition Classes
Any
val name: String

The name of the classifier
The name of the classifier

Definition Classes
NaiveBayesClassifier → Classifier
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
var params: List[List[List[Double]]]

Parameters for the likelihood for each class and feature
def predict(X: List[List[Double]]): List[Int]

Applies the trained classifier to a dataset
Applies the trained classifier to a dataset
X
List of data instances
returns
List of predictions

Definition Classes
NaiveBayesClassifier → Classifier
var prior: List[Double]

Prior probability for the classes
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(X: List[List[Double]], y: List[Int], sampleWeight: List[Double] = Nil): Unit

Performs the training of the classifier
Performs the training of the classifier
X
List of training instances
y
List of training labels

Definition Classes
NaiveBayesClassifier → Classifier
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 NaiveBayesClassifier | package classifiers

class NaiveBayesClassifier extends Classifier

Instance Constructors

new NaiveBayesClassifier(json: JsValue)

new NaiveBayesClassifier(model: String = NaiveBayesClassifier.model, priors: List[Double] = NaiveBayesClassifier.priors, alpha: Double = NaiveBayesClassifier.alpha, degree: Int = NaiveBayesClassifier.degree)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def _predict(X: List[List[Double]]): List[Int]

def _train(X: List[List[Double]], y: List[Int]): Unit

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def diagnostics(): Map[String, List[(Double, Double)]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getLikeli(x: List[Double]): List[List[Double]]

def getProbabs(x: List[Double]): List[Double]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

var params: List[List[List[Double]]]

def predict(X: List[List[Double]]): List[Int]

var prior: List[Double]

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

def toString(): String

def train(X: List[List[Double]], y: List[Int], sampleWeight: List[Double] = Nil): Unit

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped