EEML: Ensemble Explainable Machine Learning Models

In EEML: Ensemble Explainable Machine Learning Models

Ensemble Explainable Machine Learning Models

Description

Ensemble Explainable Machine Learning Models

Usage

EEML(df, Weight)

Arguments

df	List of dataframes containing various explainable scores for each model
Weight	Ensemble weights of the models (from weight function)

Value

• ImpScore: Final variable important score of EEML model

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

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)

EEML documentation built on Sept. 11, 2024, 6:54 p.m.