The Implications of Missing Data on the Stability of Random Forest and Support Vector Machine Model's Output

Domaine:

socioeconomic

Type de record:

paper

Créateur:

MosLebMog

Éditeur:

Hôte:

The study utilized two machine learning (ML) techniques namely Random Forest (RF) and Support Vector Machines (SVM) with the aim of understanding the implications of missing data on RF and SVM's ability in forecasting accuracy. The study employed a time series data sourced from South African Reserve Bank website from January 1960 to June 2021 with a total of 738 observations for variables of government expenditure as a dependent variable and government revenue as independent variable. The study employed RMSE, MSE, MAE and MAPE for measuring forecasting accuracy of these two machine learning models. Results of the study revealed that SVM outperformed RF by giving the highest prediction accuracy. Furthermore, the results show that missing data does not negatively impact the accuracy of these models since the prediction error values decreases from modelling complete data to incomplete data. The study recommends that for future research purposes other machine learning models to be explored and that other scenarios in the data including bias and outliers to be considered.

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