XgbPCFit {DSWE} | R Documentation |
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. 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)
[Package DSWE version 1.8.2 Index]