A comparison of model choice strategies for logistic regression

Purpose:The purpose of this study is to develop and compare model choice strategies in context of logistic regression.Model choice means the choice of the covariates to be included in the model.Design/methodology/approach:The study is based on Monte Carlo simulations.The methods are compared in terms of three measures of accuracy:specificity and two kinds of sensitivity.A loss function combining sensitivity and specificity is introduced and used for a final comparison.Findings:The choice of method depends on how much the users emphasize sensitivity against specificity.It also depends on the sample size.For a typical logistic regression setting with a moderate sample size and a small to moderate effect size,either BIC,BICc or Lasso seems to be optimal.Research limitations:Numerical simulations cannot cover the whole range of data-generating processes occurring with real-world data.Thus,more simulations are needed.Practical implications:Researchers can refer to these results if they believe that their data-generating process is somewhat similar to some of the scenarios presented in this paper.Alternatively,they could run their own simulations and calculate the loss function.Originality/value:This is a systematic comparison of model choice algorithms and heuristics in context of logistic regression.The distinction between two types of sensitivity and a comparison based on a loss function are methodological novelties.

Utilization of Logistical Regression to the Modified Sine-Gordon Model in the MST Experiment

In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.

Composition Analysis and Identification of Ancient Glass Products Based on L1 Regularization Logistic Regression

In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.

Enhancing PDF Malware Detection through Logistic Model Trees

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection approaches.The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies.The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree.Using a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision Tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient Descent.Beyond traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware detection.The use of Logistic Model Tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus detection.The paper emphasizes Logistic Model Tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the sector.The results reveal that the Logistic Model Tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

Integration of Multiple Spectral Data via a Logistic Regression Algorithm for Detection of Crop Residue Burned Areas:A Case Study of Songnen Plain,Northeast China

The burning of crop residues in fields is a significant global biomass burning activity which is a key element of the terrestrial carbon cycle,and an important source of atmospheric trace gasses and aerosols.Accurate estimation of cropland burned area is both crucial and challenging,especially for the small and fragmented burned scars in China.Here we developed an automated burned area mapping algorithm that was implemented using Sentinel-2 Multi Spectral Instrument(MSI)data and its effectiveness was tested taking Songnen Plain,Northeast China as a case using satellite image of 2020.We employed a logistic regression method for integrating multiple spectral data into a synthetic indicator,and compared the results with manually interpreted burned area reference maps and the Moderate-Resolution Imaging Spectroradiometer(MODIS)MCD64A1 burned area product.The overall accuracy of the single variable logistic regression was 77.38%to 86.90%and 73.47%to 97.14%for the 52TCQ and 51TYM cases,respectively.In comparison,the accuracy of the burned area map was improved to 87.14%and 98.33%for the 52TCQ and 51TYM cases,respectively by multiple variable logistic regression of Sentind-2 images.The balance of omission error and commission error was also improved.The integration of multiple spectral data combined with a logistic regression method proves to be effective for burned area detection,offering a highly automated process with an automatic threshold determination mechanism.This method exhibits excellent extensibility and flexibility taking the image tile as the operating unit.It is suitable for burned area detection at a regional scale and can also be implemented with other satellite data.

Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

This research introduces a novel approach to improve and optimize the predictive capacity of consumer purchase behaviors on e-commerce platforms. This study presented an introduction to the fundamental concepts of the logistic regression algorithm. In addition, it analyzed user data obtained from an e-commerce platform. The original data were preprocessed, and a consumer purchase prediction model was developed for the e-commerce platform using the logistic regression method. The comparison study used the classic random forest approach, further enhanced by including the K-fold cross-validation method. Evaluation of the accuracy of the model’s classification was conducted using performance indicators that included the accuracy rate, the precision rate, the recall rate, and the F1 score. A visual examination determined the significance of the findings. The findings suggest that employing the logistic regression algorithm to forecast customer purchase behaviors on e-commerce platforms can improve the efficacy of the approach and yield more accurate predictions. This study serves as a valuable resource for improving the precision of forecasting customers’ purchase behaviors on e-commerce platforms. It has significant practical implications for optimizing the operational efficiency of e-commerce platforms.

Country-based modelling of COVID-19 case fatality rate:A multiple regression analysis

BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.

Modeling of Total Dissolved Solids (TDS) and Sodium Absorption Ratio (SAR) in the Edwards-Trinity Plateau and Ogallala Aquifers in the Midland-Odessa Region Using Random Forest Regression and eXtreme Gradient Boosting

Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R2 values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.

A Hybrid Model Evaluation Based on PCA Regression Schemes Applied to Seasonal Precipitation Forecast

Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.

Logistic Regression Trust–A Trust Model for Internet-of-Things Using Regression Analysis

Internet of Things(IoT)is a popular social network in which devices are virtually connected for communicating and sharing information.This is applied greatly in business enterprises and government sectors for delivering the services to their customers,clients and citizens.But,the interaction is success-ful only based on the trust that each device has on another.Thus trust is very much essential for a social network.As Internet of Things have access over sen-sitive information,it urges to many threats that lead data management to risk.This issue is addressed by trust management that help to take decision about trust-worthiness of requestor and provider before communication and sharing.Several trust-based systems are existing for different domain using Dynamic weight meth-od,Fuzzy classification,Bayes inference and very few Regression analysis for IoT.The proposed algorithm is based on Logistic Regression,which provide strong statistical background to trust prediction.To make our stand strong on regression support to trust,we have compared the performance with equivalent sound Bayes analysis using Beta distribution.The performance is studied in simu-lated IoT setup with Quality of Service(QoS)and Social parameters for the nodes.The proposed model performs better in terms of various metrics.An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services.The most salient features of IoT system is to design it with scalability,extendibility,compatibility and resiliency against attack.The existing worksfinds a way to integrate direct and indirect trust to con-verge quickly and estimate the bias due to attacks in addition to the above features.

Application of Regularized Logistic Regression and Artificial Neural Network Model for Ozone Classification across El Paso County, Texas, United States

This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. The LR and ANN algorithms are employed to train the datasets. The models demonstrate a remarkably high classification accuracy of 89.3% in predicting ozone levels on a given day. Evaluation metrics reveal that both the ANN and LR models exhibit accuracies of 89.3% and 88.4%, respectively. Additionally, the AUC values for both models are comparable, with the ANN achieving 95.4% and the LR obtaining 95.2%. The lower the cross-entropy loss (log loss), the higher the model’s accuracy or performance. Our ANN model yields a log loss of 3.74, while the LR model shows a log loss of 6.03. The prediction time for the ANN model is approximately 0.00 seconds, whereas the LR model takes 0.02 seconds. Our odds ratio analysis indicates that features such as “Solar radiation”, “Std. Dev. Wind Direction”, “outdoor temperature”, “dew point temperature”, and “PM10” contribute to high ozone levels in El Paso, Texas. Based on metrics such as accuracy, error rate, log loss, and prediction time, the ANN model proves to be faster and more suitable for ozone classification in the El Paso, Texas area.

Predictive Modeling for Analysis of Coronavirus Symptoms Using Logistic Regression

This paper presents a case study on the IPUMS NHIS database,which provides data from censuses and surveys on the health of the U.S.population,including data related to COVID-19.By addressing gaps in previous studies,we propose a machine learning approach to train predictive models for identifying and measuring factors that affect the severity of COVID-19 symptoms.Our experiments focus on four groups of factors:demographic,socio-economic,health condition,and related to COVID-19 vaccination.By analysing the sensitivity of the variables used to train the models and the VEC(variable effect characteristics)analysis on the variable values,we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.

影响结核性胸腔积液并发胸膜增厚的非条件Logistic回归分析

目的探讨影响结核性胸腔积液并发胸膜增厚的相关危险因素。方法回顾性分析2019年1月至2021年3月广州市胸科医院呼吸内科收治的123例结核性胸腔积液患者的临床资料,统计患者胸膜增厚情况,采用单因素及非条件Logistic回归法分析患者胸膜增厚的影响因素。结果胸膜未增厚与胸膜增厚患者性别、年龄、肺结核、胸水分布、用力肺活量(FVC)、第1秒用力呼气容积(FVE1)/FVC、胸水腺苷脱氨酶(ADA)、血清ADA、胸水白细胞、胸水淋巴细胞、胸水中性粒细胞和淋巴细胞比例比较差异无统计学意义;胸膜未增厚与胸膜增厚患者胸水量、胸水吸收时间、FVE1、胸水乳酸脱氢酶(LDH)、血清LDH、胸水蛋白、血清蛋白比较差异有统计学意义(P<0.05)。Logistic回归分析结果显示,胸水量(中大量)、FVE1、胸水LDH、血清LDH、胸水蛋白及血清蛋白是胸膜增厚发生的危险因素(P<0.05)。结论结核性胸腔积液并发胸膜增厚是多因素作用的结果,胸水量、FVE1、胸水吸收时间、胸水LDH、血清LDH、胸水蛋白和血清蛋白与胸膜增厚的发生密切相关,建议临床予以密切监测并积极采取针对性干预措施。

卵圆孔未闭合并偏头痛患者头痛程度影响因素的logistic回归分析

目的分析卵圆孔未闭(PFO)合并偏头痛患者头痛程度影响因素的logistic回归分析。方法选取2022年1—12月商丘市第一人民医院就诊的100例PFO合并偏头痛患者作为研究对象,采用视觉模拟评分法(VAS)分为轻度头痛组(58例)和中重度疼痛组(42例),采用多因素logistic回归分析影响PFO合并偏头痛患者头痛程度的因素。结果单因素分析显示,轻度头痛组和中重度疼痛组吸烟史、高血压史、睡眠质量、情绪变化、PFO右向左分流量、PFO直径、PFO隧道长度及有无房间隔膨出瘤差异有统计学意义(P<0.05)。logistic多因素回归分析显示,吸烟史、高血压史、睡眠质量差、情绪变化、PFO右向左分流量大、PFO直径大、PFO隧道短及房间隔膨出瘤是影响PFO合并偏头痛患者头痛程度的影响因素(P<0.05)。结论吸烟史、高血压史、睡眠质量差、情绪变化、PFO右向左分流量大、PFO直径大、PFO隧道短及房间隔膨出瘤可影响PFO合并偏头痛患者头痛程度,临床应根据上述因素进行针对性干预,以缓解偏头痛患者头痛程度。

基于Logistic回归的国际时尚品牌销售渠道选择影响因素分析

为了帮助国际时尚品牌合理、科学地选择销售渠道和制定销售策略,以满足不同地区不同类型消费者的需求,提高品牌市场竞争力。首先依据访谈数据确定分析因子,建立影响国际时尚品牌销售渠道选择的多因素分析理论模型,提出可能影响国际品牌销售渠道选择的消费者偏好分析指标体系,从消费者个人特征、心理偏好、品类偏好以及时尚认知4个维度设计问卷并进行调研。运用SPSS软件对问卷回收数据进行录入,并采用Logistic回归分析数据,从而验证模型假设。通过分析结果可知,以上4个维度下的诸多影响因子均对国际品牌销售渠道的选择产生显著影响,并给出了具体的指导策略。品牌商可以根据研究结果制定对应的销售策略,以有效提高销售渠道利用率,最大化提升品牌销售业绩。

X线联合超声Logistic模型预测乳腺癌腋窝淋巴结转移的价值分析

目的分析与乳腺癌腋窝淋巴结转移相关的X线和超声征象,构建Logistic回归模型并评估其对术前预测腋窝淋巴结状态的临床价值。方法选取2015年1月至2022年1月高密市人民医院收治的312例原发性乳腺癌患者作为研究对象,根据是否发生腋窝淋巴结转移(ALNM)分为转移组(n=141)与未转移组(n=171)。所有患者均行X线及超声检查,比较未转移组与转移组乳腺浸润性导管癌的X线征象、超声征象,采用多因素Logistic回归分析ALNM的影响因素;绘制ROC曲线分析X线、超神征象及Logistic回归模型预测乳腺癌腋窝淋巴结转移的价值。结果两组X线原发灶长径、皮肤增厚、乳头回缩、淋巴结门和淋巴结密度比较差异有统计学意义(P<0.05),两组象限位置、钙化和边缘毛刺比较差异无统计学意义;两组超声原发灶长径、淋巴结皮髓质分界和淋巴结皮质比较差异有统计学意义(P<0.05),两组原发灶高回声晕、后场回声、血流分级和纵横比比较差异无统计学意义。多因素Logistic回归分析结果显示,X线征象的乳房皮肤增厚征象、超声淋巴结皮质增厚征象是乳腺浸润性导管癌患者发生ALNM的独立危险因素(P<0.05)。ROC曲线分析结果显示,X线乳房皮肤增厚征象和超声征象的淋巴结皮质增厚预测淋巴结转移的AUC分别为0.652(95%CI:0.589~0.714)、0.725(95%CI:0.666~0.784),模型预测ALNM的AUC为0.795(95%CI:0.742~0.848),预测效能较好。结论乳腺癌患者的X线皮肤增厚征象和超声腋窝淋巴结皮质增厚征象与ALNM有关,X线联合超声的Logistic模型可较准确地预测乳腺癌患者的腋窝淋巴结状态。

基于Logistic模型和SPC算法的关键技术识别与预测——以卫星互联网为例

识别关键技术有助于在制定技术和市场战略布局中获得先发优势,对我国经济发展、国家安全具有重要意义。文中提出一种融合生命周期与主路径分析的关键技术识别方法,是对已有的研究理论和方法的重要补充。首先,检索相关领域专利数据并进行技术划分;然后,综合运用Lo-gistic模型和SPC算法构建技术轨道,识别关键技术;接着,使用显性技术优势指数比较主要国家技术竞争态势;最后,结合关键技术与技术竞争态势绘制技术路线图,识别技术热点与前沿预测。以卫星互联网为例,阐述该方法的应用过程。研究结果表明,该方法能够较为准确识别出该领域关键技术,揭示全球技术竞争态势,有效实现技术热点识别和未来技术预测;识别出的卫星互联网关键技术,能够为国家发展政策制定与相关产业布局提供参考。

大学生“慢就业”现象及其影响因素——基于二元Logistic回归模型的实证研究

通过对32所高校调研分析发现,大学生“慢就业”现象呈现上升态势。研究表明:个人最高学历、职业规划清晰程度、就业主动性、父母对于子女选择“慢就业”的态度、是否为独生子女、家庭人均年收入、学校层次、学校就业指导服务质量等因素均显著影响大学生对于“慢就业”行为的认知与选择。为此,必须结合大学生“慢就业”行为选择的影响因素,开展“广谱式”的职业生涯规划教育、构建家校联合的就业指导机制、形成具有强针对性的就业指导体系。

影响阳虚体质因素的Logistic回归分析

目的:研究影响阳虚体质的主要因素。方法:采用横断面调查,收集上海中医药大学附属岳阳中西医结合医院治未病中心2020年9月至2021年11月中医体质调查数据。中医体质调查采用中医体质辨识软件V3.0实施,运用多因素Logistic回归模型分析筛选影响阳虚体质的主要因素。结果:共纳入811例研究对象,其中阳虚质211例(26.0%)。多因素Logistic回归分析结果显示,相对女性,男性阳虚质比例降低,差异具有统计学意义[OR=0.55,95%CI(0.37~0.81),P=0.002];相对年龄<50岁,年龄≥50岁者阳虚质比例较高,差异具有统计学意义[OR=1.83,95%CI(1.26~2.65),P=0.001];体质量超重、肥胖人群阳虚质比例高于体质量正常及偏低人群,差异具有统计学意义[OR=0.59,95%CI(0.38~0.91),P=0.018];高脂血症患者阳虚质比例高于血脂正常人群,差异具有统计学意义[OR=0.63,95%CI(0.44~0.90),P=0.011];脂肪性肝病患者阳虚质比例高于正常人群,差异具有统计学意义[OR=0.56,95%CI(0.37~0.87),P=0.010];功能性胃肠病阳虚质比例高于正常人群,差异具有统计学意义[OR=1.77,95%CI(1.05~2.99),P=0.032]。结论:年龄、性别、超重/肥胖、功能性胃肠病、脂肪性肝病、高脂血症是影响阳虚质的主要因素。