EEML (version 0.1.1)

EEML: Ensemble Explainable Machine Learning Models

Description

Ensemble Explainable Machine Learning Models

Usage

EEML(df, Weight)

Value

• ImpScore: Final variable important score of EEML model

Arguments

df

List of dataframes containing various explainable scores for each model

Weight

Ensemble weights of the models (from weight function)

References

• 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.

Examples

# \donttest{
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)
# }