kumulant
kumulant/com.eignex.kumulant.schema/RandomForestClassifier

RandomForestClassifier

@Serializable
@SerialName(value = "RandomForestClassifier")
data class RandomForestClassifier(val featureSize: Int, val numClasses: Int, val splitCandidates: List<Split>, val nbrTrees: Int = 10, val config: ClassificationTreeConfig = ClassificationTreeConfig(), val bagging: Boolean = true, val randomSeed: Int = 0) : RegressionStatSpec<ForestClassificationResult> (source)

Spec for RandomForestClassifierStat: ensembled VFDT classification forest.

Constructors

Link copied to clipboard
constructor(featureSize: Int, numClasses: Int, splitCandidates: List<Split>, nbrTrees: Int = 10, config: ClassificationTreeConfig = ClassificationTreeConfig(), bagging: Boolean = true, randomSeed: Int = 0)

Properties

Link copied to clipboard
val bagging: Boolean

Oza & Russell Poisson(1) per-tree reweighting.

Link copied to clipboard
val config: ClassificationTreeConfig

RegressionTree growth tunables (mtry defaults to ceil(sqrt(p)) when null).

Link copied to clipboard
val featureSize: Int

Number of input features.

Link copied to clipboard
val nbrTrees: Int

Trees in the forest.

Link copied to clipboard
val numClasses: Int

Number of classes.

Link copied to clipboard
val randomSeed: Int

PRNG seed shared across trees.

Link copied to clipboard
val splitCandidates: List<Split>

Candidate split pool.

Functions

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.filter(pred: BoolExpr): RegressionStatSpec<R>

Wrap this regression spec so updates are forwarded only when pred evaluates true.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.materialize(concurrency: Concurrency = Concurrency.None): RegressionStat<R>

Construct a live RegressionStat from a RegressionStatSpec. See SeriesStatSpec.materialize.

fun StatSpec.materialize(concurrency: Concurrency = Concurrency.None): Stat<*>

Construct a live stat from any StatSpec, dispatching on its modality. Useful for code paths (like StatSchemaDef.materialize) that iterate over an erased Map<String, StatSpec> and don't statically know the modality.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.minMaxScaleFeatures(targetLow: Double = 0.0, targetHigh: Double = 1.0): RegressionStatSpec<R>

Element-wise min-max scale a regression spec's feature vector.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.sample(rate: Double, seed: Long): RegressionStatSpec<R>

Wrap this regression spec to keep each update with probability rate; seed feeds the PRNG.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.standardScaleFeatures(): RegressionStatSpec<R>

Element-wise standardise a regression spec's feature vector.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.throttle(every: Int): RegressionStatSpec<R>

Wrap this regression spec so it only sees one in every every updates.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.transformX(expr: VectorExpr): RegressionStatSpec<R>

Wrap this regression spec so x is remapped by expr before the inner stat sees it.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.transformY(expr: ScalarExpr): RegressionStatSpec<R>

Wrap this regression spec so y is remapped by expr before the inner stat sees it.

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.weightBy(expr: ScalarExpr): RegressionStatSpec<R>

Wrap this regression spec so every update's weight is multiplied by expr.eval(0, y, v).

Link copied to clipboard
fun <R : Result> RegressionStatSpec<R>.withWeight(weight: Double): RegressionStatSpec<R>

Wrap this regression spec so every update uses weight regardless of caller input.

RandomForestClassifier

constructor(featureSize: Int, numClasses: Int, splitCandidates: List<Split>, nbrTrees: Int = 10, config: ClassificationTreeConfig = ClassificationTreeConfig(), bagging: Boolean = true, randomSeed: Int = 0)(source)

bagging

val bagging: Boolean(source)

Oza & Russell Poisson(1) per-tree reweighting.

config

val config: ClassificationTreeConfig(source)

RegressionTree growth tunables (mtry defaults to ceil(sqrt(p)) when null).

featureSize

val featureSize: Int(source)

Number of input features.

nbrTrees

val nbrTrees: Int(source)

Trees in the forest.

numClasses

val numClasses: Int(source)

Number of classes.

randomSeed

val randomSeed: Int(source)

PRNG seed shared across trees.

splitCandidates

val splitCandidates: List<Split>(source)

Candidate split pool.