Logistic regression

The issue of credit risk and deferred bank claims is one of the sensitive issues of banking industry, which can be considered as the main cause of bank failures. In recent years, the economic slowdown accompanied by inflation in Iran has led to an increase in deferred bank claims that could put the country's banking system in serious trouble. Accordingly, the current paper presents a prediction model for credit risk of real customers of Qavamin Bank Branch in Shiraz, using a combined approach of logistic regression and neural network. Therefore, the necessary examinations were carried out on a sample of 351 individuals from the real customers of the bank in the period 2011-2012. According to the information available, 17 variables were extracted including financial and non-financial variables for classifying customers into well-balanced s and ill-balanced s. Among the variables, five effective variables on credit risk were selected using the parent forward stepwise selection technique, which was used to train neural networks with three neurons in the hidden layer. the optimum cutting point was selected based on the performance curve of the system and the results of the neural network output on the test data show that the accuracy of the combined model in the classifier of well-balanced customers is .89 and in the category of ill-balanced customers is .83 that is better than the results of logistic regression and in general, it is possible to estimate the accuracy of prediction.

This paper describes a series of experiments in using logistic regression machine learning as a method for entity resolution. From these experiments the authors concluded that when a supervised ML algorithm is trained to classify a pair of entity references as linked or not linked pair, the evaluation of the model’s performance should take into account the transitive closure of its pairwise linking decisions, not just the pairwise classifications alone. Part of the problem is that the measures of precision and recall as calculated in data mining classification algorithms such as logistic regression is different from applying these measures to entity resolution (ER) results.. As a classifier, logistic regression precision and recall measure the algorithm’s pairwise decision performance. When applied to ER, precision and recall measure how accurately the set of input references were partitioned into subsets (clusters) referencing the same entity. When applied to datasets containing more than two references, ER is a two-step process. Step One is to classify pairs of records as linked or not linked. Step Two applies transitive closure to these linked pairs to find the maximally connected subsets (clusters) of equivalent references. The precision and recall of the final ER result will generally be different from the precision and recall measures of the pairwise classifier used to power the ER process. The experiments described in the paper were performed using a well-tested set of synthetic customer data for which the correct linking is known. The best F-measure of precision and recall for the final ER result was obtained by substantially increasing the threshold of the logistic regression pairwise classifier.

Ardebil plain is one of the flood points that requires the understanding of the flood potential. In this study, the flooding potential of Ardebil plain was performed using environmental parameters, observations of flood points and lack of floods and prediction algorithms were made including random forest and logistics regression. Independent parameters include DEM, Slope, Aspect, Distance from waterway, distance from dam, runoff accumulation, land use, landforms and indexes Topographic Position Index (TPI), Modified Catchment Area (MCA), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI) and Stream Power Index (SPI) Indices. The Roc-AUC assessment results showed that the RF and LR model were validated by 0.99 and 0.98, and it shows that random forest models and logistics regression have the ability to predict and prepare a flood sensitivity map in Ardebil plain. The output of parameters effective in flooding showed that the marginal areas located around the central plain of Ardabil have less flood-flooding potential than the central areas. The results also showed that by moving from the southwest of the plain to its northeast, the grade of floods increased. This increase in flooding potential around the main drainage of the plain is greater than elsewhere.

This paper gives a performance analysis of multiple vote classifiers based on meta-classification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%).

In economies where banks play a key role in aggregating savings and allocating credit to various sectors, it is crucial to evaluate the performance of the banking system using appropriate methods. This research paper presents a model for evaluating the efficiency of commercial banks listed in the Tehran Stock Exchange during the period from 2015 to 2020, with a focus on the impact of credit risk. The study employs a two-step descriptive-correlation retrospective method to rank the banks and explain the role of credit risk in their efficiency. Specifically, the efficiency of the banks is determined using inputs and outputs based on DEA (Data Envelopment Analysis) models. The calculation of efficiency using ideal SBM (Slacks-Based Measure) and DEA methods reveals that Mellat, Saderat, and Tejaret banks were the most efficient during the study period. Furthermore, Tobit and logistic regression models are used to investigate the relationship between the main determinants of credit risk and the efficiency of commercial banks. The findings indicate a statistically significant relationship between the two factors. Overall, this paper highlights the importance of evaluating the efficiency of the banking system in bank-oriented economies and provides a useful model for doing so. The research paper highlights the significant impact of credit risk on bank efficiency, emphasizing its role in shaping effective risk management strategies within the banking sector. It suggests that banks should prioritize these factors to enhance their operational efficiency.