| Type: | Package |
| Title: | Ensemble Explainable Machine Learning Models |
| Version: | 0.1.1 |
| Author: | Dr. Md Yeasin [aut], Dr. Ranjit Kumar Paul [aut, cre], Dr. Dipanwita Haldar [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat@gmail.com> |
| Description: | We introduced a novel ensemble-based explainable machine learning model using Model Confidence Set (MCS) and two stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The model combined the predictive capabilities of different machine-learning models and integrates the interpretability of explainability methods. To develop the proposed algorithm, a two-stage Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework was employed. The package has been developed using the algorithm of Paul et al. (2023) <doi:10.1007/s40009-023-01218-x> and Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | stats, MCS, WeightedEnsemble, topsis |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2024-08-01 04:31:02 UTC; YEASIN |
| Repository: | CRAN |
| Date/Publication: | 2024-08-01 08:50:10 UTC |
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)
Selection of Superior Models Using MSC Algorithm
Description
Selection of Superior Models Using MSC Algorithm
Usage
ModelSel(df, Alpha, K)
Arguments
df |
Dataframe of predicted values of models with first column as actual values |
Alpha |
Confidence level of MCS tests |
K |
Resampling length |
Value
SelModel: Name of the selected models
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.
Hansen PR, Lunde A, Nason JM, 2011. The model confidence set. Econometrica, 79(2), 453-497
Examples
library("EEML")
Actual<- as.ts(rnorm(200,100,50))
Model1<- as.ts(rnorm(200,100,50))
Model2<- as.ts(rnorm(200,100,50))
Model3<- as.ts(rnorm(200,100,50))
Model4<- as.ts(rnorm(200,100,50))
Model5<- as.ts(rnorm(200,100,50))
DF <- cbind(Actual, Model1,Model2,Model3,Model4,Model5)
SelModel<-ModelSel(df=DF, Alpha=0.2, K=1000)
Selection of Superior Models Using MSC Algorithm
Description
Selection of Superior Models Using MSC Algorithm
Usage
Weight(ModelSel, Optim = "PSO")
Arguments
ModelSel |
Dataframe of predicted values of selected models with first column as actual values |
Optim |
Optimisation technique |
Value
WeightEn: Ensemble weight of the candidate models
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")
Actual<- as.ts(rnorm(200,100,50))
Model1<- as.ts(rnorm(200,100,50))
Model2<- as.ts(rnorm(200,100,50))
Model3<- as.ts(rnorm(200,100,50))
DF <- cbind(Actual, Model1,Model2,Model3)
SelModel<-Weight(ModelSel=DF,Optim="PSO")