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Relevant bibliographies by topics / GridSearchCV

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Academic literature on the topic 'GridSearchCV'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "GridSearchCV"

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Lisnawita, Lisnawita, Guntoro Guntoro, and Loneli Costaner. "Hyperparameter Optimization of the Perceptron Algorithm for Determining the Feasibility of Research Proposals and Community Service." Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 15, no. 2 (2024): 172–81. https://doi.org/10.31849/digitalzone.v15i2.17812.

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Higher education in Indonesia includes diploma, bachelor, master, specialist, and doctoral programmes organised by universities. The Institute for Research and Community Service (LPPM) is in charge of assessing lecturers' proposals. This research aims to optimise the Perceptron algorithm to assess proposal eligibility using Turnitin plagiarism scores and reviewer scores. The optimisation results show that Perceptron accuracy reaches 99.44% to 99.63% at various training data ratios. GridSearchCV achieved 100% accuracy, while RandomisedSearchCV recorded accuracy between 98.89% to 99.63%. GridSea

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Muzayanah, Rini, Dwika Ananda Agustina Pertiwi, Muazam Ali, and Much Aziz Muslim. "Comparison of gridsearchcv and bayesian hyperparameter optimization in random forest algorithm for diabetes prediction." Journal of Soft Computing Exploration 5, no. 1 (2024): 86–91. http://dx.doi.org/10.52465/joscex.v5i1.308.

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Diabetes Mellitus (DM) is a chronic disease whose complications have a significant impact on patients and the wider community. In its early stages, diabetes mellitus usually does not cause significant symptoms, but if it is detected too late and not handled properly, it can cause serious health problems. To overcome these problems, diabetes detection is one of the solutions used. In this research, diabetes detection was carried out using Random Forest with gridsearchcv and bayesian hyperparameter optimization. The research was carried out through the stages of study literature, model developme

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Annaboina, Krishna, Samala Prasoona, Chada Ashritha, and Pesara Chakradhar Reddy. "Fraud Detection in Medical Insurance Claim Systems using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40522.

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Fraud detection in medical insurance claim systems is crucial for preserving healthcare service integrity and minimizing financial losses. This study explores the application of Support Vector Machines (SVM) enhanced by GridSearchCV for hyperparameter optimization, aiming to detect fraudulent claims effectively. The research methodology involves preprocessing a comprehensive medical insurance claims dataset, focusing on extensive feature selection and engineering to improve model performance. GridSearchCV is utilized to conduct an exhaustive search over specified parameter ranges, identifying

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Muhaimin, M. Rizal, and Fandi Yulian Pamuji. "Evaluasi Metode Single Exponential Smoothing dan Long Short-Term Memory pada Prediksi Saham Bank BRI." Digital Transformation Technology 4, no. 2 (2024): 869–75. https://doi.org/10.47709/digitech.v4i2.4948.

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Penelitian ini membahas perbandingan kinerja metode peramalan harga saham Bank BRI (BBRI) menggunakan dua pendekatan kuantitatif, yaitu metode Single Exponential Smoothing (SES) dan Long Short-Term Memory (LSTM) yang dioptimasi dengan GridSearchCV. Data historis harga saham BBRI dari periode 2019 hingga 2024 yang diperoleh dari Yahoo Finance digunakan sebagai data utama. Metode SES dipilih karena sederhana dan efektif dalam menangani data deret waktu, sedangkan metode LSTM dipilih karena kemampuannya dalam menangkap pola kompleks dan ketergantungan temporal pada data saham. GridSearchCV diguna

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Muhamad Malik Matin, Iik. "Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware." MULTINETICS 9, no. 1 (2023): 43–50. http://dx.doi.org/10.32722/multinetics.v9i1.5578.

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Random forest merpuakan algoritma machine learning yang populer digunakan untuk klasifikasi. Dalam mendeteksi malware, Random forest dapat membantu mengidentifikasi malware dengan akurasi yang baik. Namun, untuk meningkatkan performa model, diperlukan proses hyperparameter tuning. GridsearchCV adalah metode hyperparameter tuning yang memungkinkan pengguna untuk melakukan pemindaian pada sejumlah hyperparameter yang dipilih. Dalam paper ini, kami melakukan eksperimen dengan menggunakan GridsearchCV untuk melakukan hyperparameter tuning pada Random forest untuk tugas deteksi malware. Hasil ekspe

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Aprilliandhika, Wahyu, and Ferian Fauzi Abdulloh. "COMPARISON OF K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE ALGORITHM OPTIMIZATION WITH GRID SEARCH CV ON STROKE PREDICTION." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 991–1000. https://doi.org/10.52436/1.jutif.2024.5.4.1951.

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Stroke ranks second as the leading cause of death globally, with disability being the primary accompanying factor. The cause of death in stroke patients is due to the lack of an optimal stroke prediction system; therefore, identifying whether a patient is experiencing a stroke or not becomes the focus of this research. Thus, the objective of this study is to compare the performance of stroke prediction using two classification models, namely K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), with and without using the GridSearchCV optimization technique. In this experiment, the datase

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Saputra, Aries Gilang, Purwanto Purwanto, and Pujiono Pujiono. "Hyperparameter Tuning Decision Tree and Recursive Feature Elimination Technique for Improved Chronic Kidney Disease Classification." Scientific Journal of Informatics 11, no. 3 (2024): 821–30. http://dx.doi.org/10.15294/sji.v11i3.12990.

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Purpose: This study has the purpose of classifying patients with chronic kidney disease based on specific features and improving the classification models by tuning hyperparameters. This study aims to detect chronic kidney disease at an early stage. Methods: In this study, a machine learning classifier in the form of a decision tree is used to classify chronic kidney disease on the Risk Factor Prediction of Chronic Kidney Disease dataset. After that, the performance of the classifier model is improved by using feature selection, namely Recursive Feature Elimination and Hyperparameter tuning wi

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Nha Tran, Thi Thanh, Thi Dieu Thuan Tran, and Thi Thu Thuy Bui. "Integration of machine learning in 3D-QSAR CoMSIA models for the identification of lipid antioxidant peptides." RSC Advances 13, no. 48 (2023): 33707–20. http://dx.doi.org/10.1039/d3ra06690h.

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Maulana, Ikhsan, Amril Mutoi Siregar, Santi Arum Puspita Lestari, and Sutan Faisal. "OPTIMAL STUDY OF REAL-ESTATE PRICE PREDICTION MODELS USING MACHINE LEARNING." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 1149–64. https://doi.org/10.52436/1.jutif.2024.5.4.2565.

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Everyone wants a place to live, especially close to work, shopping centers, easy transportation, low crime rates and others. Pricing must also pay attention to external factors, not just the house. Determining this price is sometimes difficult for some people. Therefore, the aim of this research is to predict real-estate prices by taking these factors into account. Prediction results are very useful for sellers who have difficulty determining prices and also for prospective buyers who are confused when making financial plans to buy a house in the desired neighborhood. The dataset used in this

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Vazirani, Harsh, Xiaofeng Wu, Anurag Srivastava, Debajyoti Dhar, and Divyansh Pathak. "Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale." Sensors 24, no. 22 (2024): 7317. http://dx.doi.org/10.3390/s24227317.

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We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging mu

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Book chapters on the topic "GridSearchCV"

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Puślecki, Tobiasz, and Krzysztof Walkowiak. "Hyperparameters Optimization Using GridSearchCV Method for TinyML Models." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41630-9_7.

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Alsobhi, Aisha. "Prediction of COVID-19 Disease by ARIMA Model and Tuning Hyperparameter Through GridSearchCV." In Emerging Technologies in Data Mining and Information Security. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4052-1_54.

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Dharrao, Deepak, Aman Kumar, Supriyo Dhar, et al. "E-commerce Financial Sector Growth Prediction Using Random Forest Framework with GridSearchCV Model Optimization." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7862-1_23.

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Pattanayak, Satyabrata, and Tripty Singh. "Cardiovascular Disease Classification Based on Machine Learning Algorithms Using GridSearchCV, Cross Validation and Stacked Ensemble Methods." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12638-3_19.

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Akhtar, Faheem, Jianqiang Li, Yan Pei, Yang Xu, Asif Rajput, and Qing Wang. "Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3250-4_8.

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Kahl, Fabian, Iris Kahl, and Stephan M. Jonas. "XGBOrdinal: An XGBoost Extension for Ordinal Data." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250380.

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We propose XGBOrdinal, an extension of XGBoost designed for ordinal classification problems commonly found in fields like medicine, where outcomes are often represented as scores, scales, stages, or grades. The proposed approach builds on the theoretical method introduced by Frank and Hall (2001) to transform an ordinal classification problem into a series of binary classification problems. Evaluated on multiple datasets, XGBOrdinal outperformed XGBClassifier and XGBRegressor, as well as existing ordinal methods. The implementation is fully compatible with GridSearchCV and RandomizedSearchCV,

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Wijeratne, Ashansa Kithmini, Nirubikaa Ravikumar, Pulasthi Mithila Bandara, and Banujan Kuhaneswaran. "Prognostication of Crime Using Bagging Regression Model." In Advances in Library and Information Science. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4755-0.ch023.

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Crime is a social and economic problem that affects a country's quality of day-to-day life and economic growth. However, analyzing and forecasting crime is not a straightforward job for a law enforcement investigator to manually unravel the underlying nuances of crime data. To make this process easier and more automated, the authors present a machine-learning model for crime analysis and predictions. The authors used a London crime dataset and enhanced the data set by incorporating population density, percentage of economically inactive working age, and average monthly temperature. The pre-pro

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Conference papers on the topic "GridSearchCV"

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Darmiyati, Iin, Indah Soesanti, and Hanung Adi Nugroho. "Improving Autism Detection Using GridSearchCV for Severity Level Autism in Indonesian Children." In 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2024. http://dx.doi.org/10.1109/icicos62600.2024.10636869.

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T, Ranjani, and S. Annal Ezhil Selvi. "Comparative Analysis of ANN, XGBoost, and GridSearchCV-Tuned FNN for Diabetes Prediction." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867339.

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Quinevera, Stefanie, and Ahmad Saikhu. "Optimization of Classification Model for Early Detection of Pancreatic Cancer Using GridSearchCV and Autoencoder." In 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2024. http://dx.doi.org/10.1109/icitisee63424.2024.10730676.

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Muthulingam, Gurusigaamani Ayyanar, and Velmurugan Subbiah Parvathy. "Optimizing Brain Tumor Classification with GridSearchCV Using Multimodal Image Fusion: A Hyperparameter Tuning Approach." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11004891.

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Arifudin, Riza, Rizal Isnanto, and Budi Warsito. "Optimizing Student Performance with GridSearchCV Using Random Forest Classifier Method to Enhance Learning Outcome Prediction." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10956861.

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Mishra, Shweta, Saumya Bhadauria, and Aditya Trivedi. "Malware Detection Using Multi-Layer Perceptron Optimized by GridSearch." In 2024 IEEE 8th International Conference on Information and Communication Technology (CICT). IEEE, 2024. https://doi.org/10.1109/cict64037.2024.10899745.

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Babby, E., and K. Rajakumari. "Diagnosis of Human Heart Ailment using GridSearchCV." In 2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2024. http://dx.doi.org/10.1109/iitcee59897.2024.10467978.

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Wu, Chunzhi, Xiaofei Xue, and Yongtao Song. "Research on Cancer Diagnosis Method Based on LightGBM-Gridsearchcv." In BDE 2022: 2022 4th International Conference on Big Data Engineering. ACM, 2022. http://dx.doi.org/10.1145/3538950.3538966.

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Gill, Kanwarpartap Singh, and Rupesh Gupta. "Chronic Kidney Disease Detection Using GridSearchCV Cross Validation Method." In 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON). IEEE, 2023. http://dx.doi.org/10.1109/reedcon57544.2023.10151392.

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Hidayat, Taufik, Danny Manongga, Hendry, et al. "Performance Prediction Using Cross Validation (GridSearchCV) for Stunting Prevalence." In 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2024. http://dx.doi.org/10.1109/aims61812.2024.10512657.

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