DSWE (version 1.8.2)

XgbPCFit: 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
)

Value

a vector or numeric predictions on user provided test data

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

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. 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)