CatScoringMethod

Type Members

type CatScoringMethod = Value
class Val extends Value with Serializable

Attributes
protected
Definition Classes
Enumeration
Annotations
@SerialVersionUID()
abstract class Value extends Ordered[Value] with Serializable

Definition Classes
Enumeration
Annotations
@SerialVersionUID()
class ValueSet extends AbstractSet[Value] with SortedSet[Value] with SortedSetLike[Value, ValueSet] with Serializable

Definition Classes
Enumeration

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 Value(i: Int, name: String): Value

Attributes
protected
Definition Classes
Enumeration
final def Value(name: String): Value

Attributes
protected
Definition Classes
Enumeration
final def Value(i: Int): Value

Attributes
protected
Definition Classes
Enumeration
final def Value: Value

Attributes
protected
Definition Classes
Enumeration
final def apply(x: Int): Value

Definition Classes
Enumeration
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 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 hashCode(): Int

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

Definition Classes
Any
val majorityVote: Value

- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors.
- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors. In case of a tie, the category with the largest number of cases in the training data is the winner. If multiple categories are tied on the largest number of cases in the training data, then the category with the smallest data value (in lexical order) among the tied categories is the winner.
- weightedMajorityVote: categorical targets, predicted target corresponds to the category with the highest weighted frequency of occurrence among the k-nearest neighbors. The weights are proportional to the inverse of the distance from each k-neighbor to the query point.
final def maxId: Int

Definition Classes
Enumeration
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
var nextId: Int

Attributes
protected
Definition Classes
Enumeration
var nextName: Iterator[String]

Attributes
protected
Definition Classes
Enumeration
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def readResolve(): AnyRef

Attributes
protected
Definition Classes
Enumeration
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Enumeration → AnyRef → Any
def values: ValueSet

Definition Classes
Enumeration
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( ... )
val weightedMajorityVote: Value

- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors.
- majorityVote: categorical targets, predicted target corresponds to the category with the highest frequency of occurrence among the k-nearest neighbors. In case of a tie, the category with the largest number of cases in the training data is the winner. If multiple categories are tied on the largest number of cases in the training data, then the category with the smallest data value (in lexical order) among the tied categories is the winner.
- weightedMajorityVote: categorical targets, predicted target corresponds to the category with the highest weighted frequency of occurrence among the k-nearest neighbors. The weights are proportional to the inverse of the distance from each k-neighbor to the query point.
final def withName(s: String): Value

Definition Classes
Enumeration

Related Doc: package model

object CatScoringMethod extends Enumeration

Type Members

type CatScoringMethod = Value

class Val extends Value with Serializable

abstract class Value extends Ordered[Value] with Serializable

class ValueSet extends AbstractSet[Value] with SortedSet[Value] with SortedSetLike[Value, ValueSet] with Serializable

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def Value(i: Int, name: String): Value

final def Value(name: String): Value

final def Value(i: Int): Value

final def Value: Value

final def apply(x: Int): Value

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val majorityVote: Value

final def maxId: Int

final def ne(arg0: AnyRef): Boolean

var nextId: Int

var nextName: Iterator[String]

final def notify(): Unit

final def notifyAll(): Unit

def readResolve(): AnyRef

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

def toString(): String

def values: ValueSet

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val weightedMajorityVote: Value

final def withName(s: String): Value

Inherited from Enumeration

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped