ML.DMLC.XGBoost

Documentation

integerToFloat :: Int -> Float

valueToLabel :: IntegralRounding a => a -> Int32

Cast floating point output to integer label.

compareLabels

Arguments

:: UArray Float

Wanted

-> UArray Float

Actual output

-> Float

Successfully rate

compareLabels'

Arguments

:: UArray Float

Wanted

-> UArray Float

Actual output

-> (Float -> Float -> Bool)

decide whether two given labels are equal

-> Float

Successfully rate

data BoosterParam

Parameter passed to booster.

Constructors

Show a => Param

Fields

• paramName :: String
• paramValue :: a

data ObjectiveFunction

Predefined objective functions.

Ref: https://github.com/dmlc/xgboost/blob/master/src/objective/regression_obj.cc

Constructors

RegLinear

RegLogistic

BinaryLogistic

BinaryLogitraw

CountPoisson

RegGamma

RegTweedie

Instances

Eq ObjectiveFunction

Methods

(==) :: ObjectiveFunction -> ObjectiveFunction -> Bool

(/=) :: ObjectiveFunction -> ObjectiveFunction -> Bool

Show ObjectiveFunction

Methods

showsPrec :: Int -> ObjectiveFunction -> ShowS

show :: ObjectiveFunction -> String

showList :: [ObjectiveFunction] -> ShowS

setBoosterParam :: Booster -> BoosterParam -> IO ()

newBooster :: (?params :: [BoosterParam]) => [DMatrix] -> IO Booster

xgbTrain

Arguments

:: (?params :: [BoosterParam], ?debug :: Bool)

=> DMatrix

Data to be trained

-> Int32

Number of boosting iterations

-> IO Booster

xgbPredict

Arguments

:: (?debug :: Bool)

=> Booster

-> DMatrix

-> [PredictMask]

-> Int32

Limit number of trees in the prediction; defaults to 0 (use all trees).

-> IO (UArray Float)

module ML.DMLC.XGBoost.FFI