EEML {EEML} | R Documentation |
Ensemble Explainable Machine Learning Models
EEML(df, Weight)
df |
List of dataframes containing various explainable scores for each model |
Weight |
Ensemble weights of the models (from weight function) |
ImpScore: Final variable important score of EEML model
Paul, R.K., Das, T. and Yeasin, M., 2023. Ensemble of time series and machine learning model for forecasting volatility in agricultural prices. National Academy Science Letters, 46(3), pp.185-188.
Yeasin, M. and Paul, R.K., 2024. OptiSembleForecasting: optimization-based ensemble forecasting using MCS algorithm and PCA-based error index. The Journal of Supercomputing, 80(2), pp.1568-1597.
library("EEML")
df1<- as.data.frame(matrix(rnorm(50) , nrow = 10) )
df2<- as.data.frame(matrix(rnorm(50) , nrow = 10) )
df3<- as.data.frame(matrix(rnorm(50) , nrow = 10) )
rownames(df1)<- rownames(df2)<-rownames(df3)<-paste0("Var", seq(1,10,1))
colnames(df1)<- colnames(df2)<-colnames(df3)<-paste0("Exp", seq(1,5,1))
DF<- list(df1, df2, df3)
EEML<-EEML(df=DF,Weight=NULL)