Predicting bankruptcy of local government: A machine learning approach

Predicting vaccine hesitancy from area‐level indicators: A machine learning approach

“…In our binary classification problem, the positive class is defined as the municipality with high VH risk and the negative class is the municipality with low VH risk. The ROC curve shows the classifier diagnostic ability by plotting the TPR on the y ‐axis against the FPR on the x ‐axis since its discrimination threshold is varied (Antulov‐Fantulin et al., 2021). The TPR is the ratio of municipalities with high VH risk that were correctly categorized as high VH risk (true positive) and the total number of positive samples (high VH risk).…”

Several explorations on how to construct an early warning system for local government debt risk in China

“…With the development of software and econometric models, a new trend in the latest work is to introduce the machine learning algorithms into the EWS for municipal debt risk. Representatives are Antulov-Fantulin et al [ 21 ], Alaminos et al [ 22 ] and Zahariev et al [ 23 ]. To be specific, an optimal machine learning model has been selected by Antulov-Fantulin et al [ 21 ] from the gradient boosting machine, random forest, lasso and neural network, finding that the gradient boosting machine performs the best in predicting bankruptcy of local government in Italy.…”

“…Representatives are Antulov-Fantulin et al [ 21 ], Alaminos et al [ 22 ] and Zahariev et al [ 23 ]. To be specific, an optimal machine learning model has been selected by Antulov-Fantulin et al [ 21 ] from the gradient boosting machine, random forest, lasso and neural network, finding that the gradient boosting machine performs the best in predicting bankruptcy of local government in Italy. The fuzzy decision trees, AdaBoost, extreme gradient boosting and deep learning neural decision trees all have been proved a good early warning performance for sovereign debt crisis by Alaminos et al [ 22 ].…”

“…Moreover, due to the numerous advances in software, recent works increasingly use the machine learning algorithms to finalize prediction. Scholars have showed how machine learning algorithms outperform the traditional econometric models, and recognized the superiority of machine learning algorithms in constructing EWS (see Antulov-Fantulin et al [ 21 ], Alaminos et al [ 22 ] and Zahariev et al [ 23 ]). Based on this, this paper will use machine learning algorithms to improve the generalization performance of our EWS.…”

“…According to Andreas and Sarah [ 61 ], k-neighbors has slow prediction and cannot deal with a dataset of many feature vectors; linear classifier is limited in low dimensions in face of small sample data; neural network takes a long training time and is easily trapped in the local optimum in the small sample data. However, the random forest, gradient boosting machine and support vector machine have advantages of statistical, computational and representative, without high requirements for sample size [ 21 – 23 ]. Therefore, this paper selects random forest, gradient boosting machine and support vector machine, as detailed in Table 8 .…”