RandomForestClassifier

Instance Constructors

new RandomForestClassifier(json: JsValue)
new RandomForestClassifier(depth: Int = RandomForestClassifier.depth, criterion: String = RandomForestClassifier.criterion, minSamplesSplit: Int = ..., nEstimators: Int = RandomForestClassifier.nEstimators, subSampleSize: Double = ..., verbose: Int = RandomForestClassifier.verbose)

depth
Depth of the tree
criterion
Function to measure the quality of a split
minSamplesSplit
Minimum number of samples required to split an internal node
nEstimators
Number of boosting steps
subSampleSize
Fractional size of the sub-sample of random instances with replacement (default: number of training instances)
verbose
Verbosity of output

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
val classifiers: ParSeq[ClassifierWrapper]
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 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
RandomForestClassifier → Classifier
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
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
RandomForestClassifier → Classifier
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
RandomForestClassifier → 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 RandomForestClassifier | package classifiers

class RandomForestClassifier extends Classifier

Instance Constructors

new RandomForestClassifier(json: JsValue)

new RandomForestClassifier(depth: Int = RandomForestClassifier.depth, criterion: String = RandomForestClassifier.criterion, minSamplesSplit: Int = ..., nEstimators: Int = RandomForestClassifier.nEstimators, subSampleSize: Double = ..., verbose: Int = RandomForestClassifier.verbose)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val classifiers: ParSeq[ClassifierWrapper]

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 hashCode(): Int

final def isInstanceOf[T0]: Boolean

val name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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