Class XGBoostClassifier

eXtreme Gradient Boosting classifier

Hierarchy (View Summary)

XGBoost
- XGBoostClassifier

Index

Constructors

Properties

_cls _lambda _learning_rate _loss _maxd _r _srate _trees _x _y

Accessors

Methods

_sample fit init predict

Constructors

constructor

new XGBoostClassifier(
    maxdepth?: number,
    srate?: number,
    lambda?: number,
    lr?: number,
): XGBoostClassifier
Parameters
- Optionalmaxdepth: number
  Maximum depth of tree
- Optionalsrate: number
  Sampling rate
- Optionallambda: number
  Regularization parameter
- Optionallr: number
  Learning rate
Returns XGBoostClassifier
Inherited from XGBoost.constructor
- Defined in lib/model/xgboost.d.ts:11

Properties

_cls

_cls: any[]

_lambda

_lambda: number

_learning_rate

_learning_rate: number

_loss

_loss: Matrix<number[]>

_maxd

_maxd: number

_r

_r: any[]

_srate

_srate: number

_trees

_trees: any[]

_x

_x: number[][]

_y

_y: Matrix<number[]>

Accessors

size

get size(): number
Number of trees

Returns number
Inherited from XGBoost.size
- Defined in lib/model/xgboost.d.ts:22

Methods

_sample

_sample(n: any): number[]
Parameters
- n: any
Returns number[]
Inherited from XGBoost._sample
- Defined in lib/model/xgboost.d.ts:23

fit

fit(): void
Fit model.

Returns void
Inherited from XGBoost.fit
- Defined in lib/model/xgboost.d.ts:36

init

init(x: number[][], y: any[]): void
Initialize model.
Parameters
- x: number[][]
  Training data
- y: any[]
  Target values
Returns void
Overrides XGBoost.init
- Defined in lib/model/xgboost.d.ts:53

predict

predict(x: number[][]): any[]
Returns predicted categories.
Parameters
- x: number[][]
  Sample data
Returns any[]
Predicted values
Overrides XGBoost.predict
- Defined in lib/model/xgboost.d.ts:60