Relevant bibliographies by topics / LGBM Regressor

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Academic literature on the topic 'LGBM Regressor'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "LGBM Regressor"

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AMIT BIJLWAN, SHWETA POKHRIYAL, RAJEEV RANJAN, R.K SINGH, and ANKITA JHA. "Machine learning methods for estimating reference evapotranspiration." Journal of Agrometeorology 26, no. 1 (2024): 63–68. http://dx.doi.org/10.54386/jam.v26i1.2462.

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Precise estimation of evapotranspiration is crucial for optimizing crop water uses particularly in the context of agriculture and horticultural production. In this study, various machine learning techniques was used to determine reference evapotranspiration by leveraging historical weather data. The models tested include artificial neural networks (ANN), Lasso, Ridge, Random Forest, LGBM regressor, and Gradient boosting regressor. LGBM regressor emerged as the top-performing model, exhibiting exceptional accuracy with a testing R-squared of 1.0. ANN also demonstrated notable performance, achie
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Shivraj, R., S. Vikas, Abhishek MN Naga, Kumar GN Naveen, Deepak NR Dr., and B. Ompraksash. "Prediction of Stock Market Performance Analysis by Using Machine Learning Regressor Techniques." Recent Trends in Computer Graphics and Multimedia Technology 7, no. 2 (2025): 11–21. https://doi.org/10.5281/zenodo.15331553.

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<em>Stock market prediction is a widely researched and crucial topic for investors, traders, and financial analysts. Precisely predicting stock price fluctuations can aid in making informed decisions regarding the buying or selling of stocks. One approach to achieving this is through sentimental analysis that has emerged as a popular approach for predicting stock prices. The research employs machine learning methods to enhance the accuracy of stock market predictions. It focuses on analyzing the efficiency of five advanced machine learning regression model.</em> <em>Bagging Regressor, XGB Regr
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Lubis, Fachrul Rozi, and Eddy Rahman Syahputra. "Peramalan Deret Waktu untuk Bisnis : Pendekatan algoritma LGBM Regressor." Data Sciences Indonesia (DSI) 1, no. 2 (2022): 69–75. http://dx.doi.org/10.47709/dsi.v1i2.1347.

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Peramalan deret waktu adalah topik yang cukup umum di bidang data science (ilmu data). Perusahaan menggunakan model peramalan untuk mendapatkan pandangan yang lebih jelas tentang bisnis masa depan. Data masa lalu dikumpulkan dan dianalisis melalui model kuantitatif atau kualitatif sehingga pola dapat diidentifikasi dan dapat mengarahkan perencanaan bisnis di masa depan akan tetapi memilih algoritme yang tepat merupakan salah satu keputusan sulit ketika akan mengembangkan model peramalan deret waktu. Penelitian ini menyajikan hasil analisi data dengan mengadopsi kerangka kerja data science CRIS
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Geyikoğlu, Ali, and Mete Yağanoğlu. "Makine Öğrenmesi Algoritmaları ile Elektrik Dağıtım Şebekeleri Arıza Tahmini." Karadeniz Fen Bilimleri Dergisi 15, no. 1 (2025): 73–98. https://doi.org/10.31466/kfbd.1482179.

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Elektrik dağıtım şebekelerinde arıza; kaliteli ve sürekli enerji akışını engelleyici faktörler olarak tanımlanmaktadır. Arızanın meydana gelmesi sonrasında Elektrik Dağıtım Şirketleri, bakım-onarım ve yatırım çalışmaları ile düzeltici faaliyetler gerçekleştirmektedir. Meydana gelen arızalar ve sonrası düzeltici faaliyetler ile teknik kalite parametreleri sistemlerce oluşturulmaktadır. Ancak ortaya çıkan teknik veriler, herhangi bir tahminleme altyapısında kullanılmamakta, düzeltici faaliyetler genel olarak yorum ve taleplere istinaden gerçekleştirilmektedir. Bu çalışmada, sezgisel yaklaşımları
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Febriyanti, Ama, and Tomy Rizky Izzalqurny. "Predicting Credit Paying Ability with Machine Learning Algorithms." Majalah Bisnis & IPTEK 16, no. 1 (2023): 8–15. https://doi.org/10.55208/781ypr87.

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Most people still have difficulty accessing finance because of a lack or even no credit history. This study aims to develop a data model that predicts a customer's ability to pay from various aspects other than credit history. This study uses the CRSIP-DM (Cross Industry Standard Process Model for Data mining) method. The data used in this study is the Home Credit Default Risk dataset collected by documentation techniques. The data were then analyzed using data modeling analysis techniques, namely logistic regressor, decision tree classifier, random forest classifier, and lgbm classifier. This
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Febriyanti, Ama, and Tomy Rizky Izzalqurny. "Predicting Credit Paying Ability With Machine Learning Algorithms." Majalah Bisnis & IPTEK 16, no. 1 (2023): 8–15. http://dx.doi.org/10.55208/bistek.v16i1.296.

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Most people still have difficulty accessing finance because of a lack or even no credit history. This study aims to develop a data model that predicts a customer's ability to pay from various aspects other than credit history. This study uses the CRSIP-DM (Cross Industry Standard Process Model for Data mining) method. The data used in this study is the Home Credit Default Risk dataset collected by documentation techniques. The data were then analyzed using data modeling analysis techniques, namely logistic regressor, decision tree classifier, random forest classifier, and lgbm classifier. This
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Phyo, Pyae-Pyae, Yung-Cheol Byun, and Namje Park. "Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression." Symmetry 14, no. 1 (2022): 160. http://dx.doi.org/10.3390/sym14010160.

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Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final pred
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Thi Thanh Hang, Hoang, Nguyen Thi Kim Phung, Tran Trong Huy, and Lê Thị Phượng Liên. "Impact of Macro and Micro Factors on Provision for Credit Risks of Commercial Banks in Vietnam: Approach on Python Programming Platform." International Journal of Business & Management Studies 04, no. 05 (2023): 26–34. http://dx.doi.org/10.56734/ijbms.v4n5a3.

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This study uses LGBM Regressor (Light Gradient Boosting Machine Regressor) algorithm in machine learning on python platform along with SHAP (Shapley Additive exPlanation) technique to extract information from machine learning model to evaluate the macro and micro factors affecting the provision for credit risks at commercial banks in Vietnam. Data was collected from 30 commercial banks in Vietnam from 2008 to 2020. Research results show that profitability, size, bad debt, credit balance, capital adequacy ratio, economic growth and unemployment rate have an impact on the provision for credit ri
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Leleń, Michał, Katarzyna Biruk-Urban, Jerzy Józwik, and Paweł Tomiło. "Modeling and Machine Learning of Vibration Amplitude and Surface Roughness after Waterjet Cutting." Materials 16, no. 19 (2023): 6474. http://dx.doi.org/10.3390/ma16196474.

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This study focused on analyzing vibrations during waterjet cutting with variable technological parameters (speed, vfi; and pressure, pi), using a three-axis accelerometer from SEQUOIA for three different materials: aluminum alloy, titanium alloy, and steel. Difficult-to-machine materials often require specialized tools and machinery for machining; however, waterjet cutting offers an alternative. Vibrations during this process can affect the quality of cutting edges and surfaces. Surface roughness was measured by contact methods after waterjet cutting. A machine learning (ML) model was develope
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Nguyen, Huu Nam, Quoc Thanh Tran, Canh Tung Ngo, Duc Dam Nguyen, and Van Quan Tran. "Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms." PLOS ONE 20, no. 1 (2025): e0315955. https://doi.org/10.1371/journal.pone.0315955.

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Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (Ligh
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Book chapters on the topic "LGBM Regressor"

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Purna Syam Chand S and G. Divya. "A Light Gradient Boosting Machine Regression Model for Prediction of Agriculture Insurance Cost over Linear Regression." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220027.

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To increase accuracy for the prediction of agriculture insurance claim cost based on crop insurance data.Gradient Boosting Machine (LGBM) and linear regression models are tested with total Samples 6022 for n=7 iterations to predict accuracy. LGBM works based on decision tree algorithm and linear based on fitted regression equation. The coefficient of determination values of proposed LGBM regression (92.52%) and linear regression (72.47%) are obtained. There was a statistical significance between LGBM regression and linear regression (p=0.001).Prediction of agriculture insurance claim cost LGBM regression technique produces significantly better performance than the linear regression technique.
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Varna, Chinthapatla Pranay, Mannipudi Prabhu Das, Gurram Sunitha, A. V. Sriharsha, and Mohammad Gouse Galety. "Predictive Modeling in Finance." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-6215-0.ch003.

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Predicting financial stability is crucial for assessing risk and making informed decisions in the financial sector. Accurate predictions can help prevent financial crises and guide strategic planning for companies and investors. Various machine learning algorithms have been employed to enhance prediction accuracy for economic distress, including XGB, LGBM, Linear Discriminant Analysis, and Logistic Regression. These models were assessed based on key performance metrics: Accuracy, ROC AUC, and F1 Score. The result revealed that LDA excels with an ROC AUC of 0.90 and an F1 Score of 0.98, demonstrating its superior ability to balance precision and recall and effectively differentiate between distressed and non-distressed entities. While the XGB Classifier and LGBM Classifier also show strong performance, they do not exceed LDA in overall effectiveness. These results highlight the importance of leveraging multiple evaluation metrics to select the most suitable model, with LDA emerging as the most reliable choice for accurate financial distress predictions.
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Raghavendra Reddy P and Sivanesh Kumar A. "A Fraudulent Transaction Prediction in Credit Card by Using Novel LGBA over LR Algorithms." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220073.

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The Research is to predict the accuracy of credit card fraudulent transactions using the Light Gradient Booster algorithm.Novel Light Gradient Booster with sample size =10 then Logistic Regression of sample size = 10 were executed for estimating accuracy rate of credit card fraudulent transactions. The sigmoid functions used in Light Gradient maps the values between 0 and 1. Light Gradient Booster Algorithm has a Maximum accuracy (91.6%) while comparing to the performance of Logistic Regression (81.4%). There was a statistical significance difference between Light Gradient Booster and Logistic Regression with p = 0.0001 (p&lt;0.05) based on 2-tailed analysis. Light Gradient algorithm helps in predicting with better accuracy percentage of credit card fraudulent transaction than Logistic regression.
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Palaniswamy, Swathypriyadharsini, Chitradevi T. N., Prabha Devi D., and K. Premalatha. "Ensemble-Based Machine Learning Techniques for Adaptive Wireless Sensor Networks." In Advances in Computer and Electrical Engineering. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7600-3.ch015.

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Wireless sensor networks (WSN) have gained popularity in next-generation IoT connectivity due to their sustainability and low maintenance. However, the dynamic nature of energy sources and environmental conditions presents challenges to the security and reliability of WSNs, particularly in mitigating various network attacks. Machine learning offers solutions to these challenges by enabling adaptive real-time behaviour. This chapter addresses the challenges of WSN security by applying ML techniques to a multi-class WSN dataset attacks such as normal, flooding, TDMA, grayhole, and blackhole. SMOTE is applied to manage class imbalance, and an ensemble framework is proposed with classifiers such as logistic regression, random forest, gradient boost, xtreme gradient boost, decision tree, LGBM, SVM, and CatBoost were applied to predict attacks in WSN-DS dataset. The models are rigorously tested and evaluated using accuracy, precision, recall, and F1-score. Gradient boost, xtreme gradient boost, and catboost classifiers outperform all other classifiers by achieving 98% of accuracy.
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Parikh, Shilpi Hiteshkumar, Anushka Gaurang Sandesara, and Chintan Bhatt. "Network Intrusion Detection Using Linear and Ensemble ML Modeling." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-6988-7.ch003.

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Network attacks are continuously surging, and attackers keep on changing their ways in penetrating a system. A network intrusion detection system is created to monitor traffic in the network and to warn regarding the breach in security by invading foreign entities in the network. Specific experiments have been performed on the NSL-KDD dataset instead of the KDD dataset because it does not have redundant data so the output produced from classifiers will not be biased. The main types of attacks are divided into four categories: denial of service (DoS), probe attack, user to root attack (U2R), remote to local attack (R2L). Overall, this chapter proposes an intense study on linear and ensemble models such as logistic regression, stochastic gradient descent (SGD), naïve bayes, light GBM (LGBM), and XGBoost. Lastly, a stacked model is developed that is trained on the above-mentioned classifiers, and it is applied to detect intrusion in networks. From the plethora of approaches taken into consideration, the authors have found maximum accuracy (98.6%) from stacked model and XGBoost.
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Sharif, Samane, Raheleh Ghouchan Nezhad Noor Nia, Hassan Abbassian, and Saeid Eslami. "Comparison of Regression Methods to Predict the First Spike Latency in Response to an External Stimulus in Intracellular Recordings for Cerebellar Cells." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240531.

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The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine learning techniques to predict first spike latency from whole cell patch recording data. Experiments were conducted on Control (Salin) and Experiment (Harmaline) groups, generating a dataset for developing predictive models. Because the dataset has a limited number of samples, we utilized models that are effective with small datasets. Among different groups of regression models (linear, ensemble, and tree models), the ensemble models, specifically the LGB method, can achieve better performance. The results demonstrate accurate prediction of first spike latency, with an average mean squared error of 0.0002 and mean absolute error of 0.01 in 10-fold cross-validation. The research suggests the potential of machine learning in forecasting the first spike latency, allowing reliable estimation without the need for extensive animal testing. This intelligent predictive system facilitates efficient analysis of first spike latency changes in both healthy and unhealthy brain cells, streamlining experimentation and providing more detailed insights into the captured signals.
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Conference papers on the topic "LGBM Regressor"

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Chao, Nicholas Vincent, Rafael Jo, Ghinaa Zain Nabiilah, and Jurike V. Moniagaa. "Thread User Sentiment Analysis Based on Text Using LGBM, SVM, and Logistic Regression Algorithm." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903786.

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Zhao, Zhongyang, Long Zhang, Junshuai Zhang, and Chenyang Li. "Prognostic prediction and analysis of key factors: a study of haemorrhagic stroke based on LGBM regression and structural equation modelling Prognostic Prediction of Haemorrhagic Stroke Factors LGBM Regression and Structural Equation Modelling Analysis." In BIC 2024: 2024 4th International Conference on Bioinformatics and Intelligent Computing. ACM, 2024. http://dx.doi.org/10.1145/3665689.3665727.

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Talapatra, Akash, Bahareh Nojabaei, and Pooya Khodaparast. "A Data-Based Continuous and Predictive Viscosity Model for the Oil-Surfactant-Brine Microemulsion Phase." In SPE Improved Oil Recovery Conference. SPE, 2024. http://dx.doi.org/10.2118/218134-ms.

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Abstract This study presents a computationally produced data-based model/correlation that can accurately estimate the magnitude and predict the peaks of microemulsion viscosity at dynamic reservoir conditions. Equilibrium molecular dynamics (MD) simulation is used on a decane-SDS-brine interfacial system to generate a dataset of viscosity values as a function of different temperatures, surfactant concentrations, and salinities. The viscosity testing and training data are computationally measured using the Einstein relation of the Green-Kubo formula. Several machine learning (ML) based regressi
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Kumarawadu, H. R., T. G. P. L. Weerasingha, and J. S. Perera. "Using machine learning to predict fire resistance of FRP strengthened concrete beams." In Civil Engineering Research Symposium 2024. Department of Civil Engineering, University of Moratuwa, 2024. http://dx.doi.org/10.31705/cers.2024.39.

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Fiber-Reinforced Polymer (FRP) materials are increasingly utilized over conventional repair techniques for reinforced concrete due to their advantageous properties, including lightweight, high strength, and corrosion resistance. However, these materials are susceptible to degradation under fire conditions, which can weaken the polymer resin, reduce material strength and stiffness, and ultimately compromise structural integrity. Given the necessity of assessing the fire resistance of FRP-strengthened beams, traditional evaluation methods, despite their accuracy, are constrained by significant t
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Bohachuk, Andrii R. "Information technology predicting the sale prices of houses in king-county by machine learning methods." In 16th IC Measurement and Control in Complex Systems. VNTU, 2022. http://dx.doi.org/10.31649/mccs2022.16.

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The sale and purchase of the real estate, in particular housing, and houses are extremely important for our life. Most people turn to real estate agencies to realtors in order to purchase quality housing and at the same time at the best price for the buyer. You should rely not only on personal assessment or assessment of third-party experts but also use price prediction systems that, using the features of the house (area, number of floors, location, number of bedrooms, year of construction, etc.), are able to predict its possible price. The report is devoted to the task of improving the accura
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Khan, Abdul Muqtadir, Abdullah BinZiad, and Abdullah Al Subaii. "Boosting Algorithm Choice in Predictive Machine Learning Models for Fracturing Applications." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205642-ms.

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Abstract With the advancement in machine learning (ML) applications, some recent research has been conducted to optimize fracturing treatments. There are a variety of models available using various objective functions for optimization and different mathematical techniques. There is a need to extend the ML techniques to optimize the choice of algorithm. For fracturing treatment design, the literature for comparative algorithm performance is sparse. The research predominantly shows that compared to the most commonly used regressors and classifiers, some sort of boosting technique consistently ou
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