  Food security is more prominent on the policy agenda today than it has been
in the past, thanks to recent food shortages at both the regional and global
levels as well as renewed promises from major donor countries to combat chronic
hunger. One field where machine learning can be used is in the classification
of household food insecurity. In this study, we establish a robust methodology
to categorize whether or not a household is being food secure and food insecure
by machine learning algorithms. In this study, we have used ten machine
learning algorithms to classify the food security status of the Household.
Gradient Boosting (GB), Random Forest (RF), Extra Tree (ET), Bagging, K-Nearest
Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), Logistic
Regression (LR), Ada Boost (AB) and Naive Bayes were the classification
algorithms used throughout this study (NB). Then, we perform classification
tasks from developing data set for household food security status by gathering
data from HICE survey data and validating it by Domain Experts. The performance
of all classifiers has better results for all performance metrics. The
performance of the Random Forest and Gradient Boosting models are outstanding
with a testing accuracy of 0.9997 and the other classifier such as Bagging,
Decision tree, Ada Boost, Extra tree, K-nearest neighbor, Logistic Regression,
SVM and Naive Bayes are scored 0.9996, 0.09996, 0.9994, 0.95675, 0.9415,
0.8915, 0.7853 and 0.7595, respectively.
