XgbPCFit: xgboost based power curve modelling

In TAMU-AML/DSWE-Package: Data Science for Wind Energy

xgboost based power curve modelling

Description

xgboost based power curve modelling

Usage

XgbPCFit(
  trainX,
  trainY,
  testX,
  max.depth = 8,
  eta = 0.25,
  nthread = 2,
  nrounds = 5
)

Arguments

trainX	a matrix or dataframe to be used in modelling
trainY	a numeric or vector as a target
testX	a matrix or dataframe, to be used in computing the predictions
max.depth	maximum depth of a tree
eta	learning rate
nthread	This parameter specifies the number of CPU threads that XGBoost
nrounds	number of boosting rounds or trees to build

Value

a vector or numeric predictions on user provided test data

References

Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1145/2939672.2939785")}.

Examples

data = data1
trainX = as.matrix(data[c(1:100),2])
trainY = data[c(1:100),7]
testX = as.matrix(data[c(101:110),2])

Xgb_prediction = XgbPCFit(trainX, trainY, testX)

TAMU-AML/DSWE-Package documentation built on Feb. 12, 2024, 11:35 p.m.