Essays about: "XGBoost model"
Showing result 1 - 5 of 108 essays containing the words XGBoost model.
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1. Optimizing Flight Ranking:A Machine Learning Approach : Applying Machine Learning to Upgrade Flight Sorting and User Experience
University essay from KTH/Hälsoinformatik och logistikAbstract : Flygresor.se, a leading flight comparison platform, uses machine learning to rankflights based on their likelihood of being clicked. The main goal of this project was toimprove this flight sorting to obtain a better user experience. The platform's existingmodel is based on a neural network approach and a limited set of features. READ MORE
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2. Predicting True Sepsis and Culture-positive Sepsis in Intensive Care Unit with Machine Learning Techniques
University essay from Lunds universitet/Matematisk statistikAbstract : Sepsis, a serious medical condition often leading to patients requiring intensive care, has prompted numerous scientists to employ mathematical techniques to aid in its diagnosis. This thesis uses logistic regression and a machine learning technique, XGBoost, to predict true sepsis (as opposed to sepsis mimics) and culture-positive sepsis (among true sepsis) in critical care using blood test results, physiological measurements and other patient characteristics. READ MORE
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3. On The Evaluation of District Heating Load Predictions
University essay from Lunds universitet/Institutionen för energivetenskaperAbstract : District Heating is a technology with the potential to enable a fossil-free society. However, to realize this potential, some improvements need to be made in order to improve District Heating operation at large, decrease losses in the systems, and thus increase the competitiveness of District Heating as a technology. READ MORE
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4. Explainable Artificial Intelligence and its Applications in Behavioural Credit Scoring
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Credit scoring is critical for banks to evaluate new loan applications and monitor existing customers. Machine learning has been extensively researched for this case; however, the adoption of machine learning methods is minimal in financial risk management. READ MORE
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5. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance
University essay from Uppsala universitet/Avdelningen för beräkningsvetenskapAbstract : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. READ MORE