Essays about: "XGBoost"

Showing result 16 - 20 of 137 essays containing the word XGBoost.

  1. 16. Improving Visibility Forecasts in Denmark Using Machine Learning Post-processing

    University essay from Uppsala universitet/Luft-, vatten- och landskapslära

    Author : August Thomasson; [2023]
    Keywords : visibility forecast; fog; machine learning; numerical weather predicition; XGBoost; Random Forest; siktprognos; dimma; maskininlärning; numerisk vädermodell; XGBoost; Random Forest;

    Abstract : Accurate fog prediction is an important task facing forecast centers since low visibility can affect anthropogenic systems, such as aviation. Therefore, this study investigates the use of Machine Learning classification algorithms for post-processing the output of the Danish Meteorological Institute’s operational Numerical Weather Prediction (NWP) model to improve visibility prediction. READ MORE

  3. 17. Enhancing House Rental Price Prediction Models for the Swedish Market : Exploring External features, Prediction intervals and Uncertainty Management in Predicting House Rental Prices

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Vasigaran Senthilkumar; [2023]
    Keywords : ;

    Abstract : Exakt förutsägelse av hyrespriserna för hus är ett avgörande problem i verkligheten fastighetsdomän, vilket underlättar informerat beslutsfattande för både hyresgäster och hyresvärdar. Denna studie presenterar en omfattande utforskning av olika maskininlärningstekniker som tillämpas på en mångsidig datauppsättning av husfunktioner, med det övergripande målet att avslöja den mest effektiva algoritmen för förutsäga hyrespriser. READ MORE

  4. 18. Forecasting copper price using VAR and the XGBoost model: an experiment with a relatively small dataset

    University essay from Lunds universitet/Nationalekonomiska institutionen; Lunds universitet/Statistiska institutionen

    Author : Juanli Hu; [2023]
    Keywords : copper price; Vector autoregressive model; XGBoost; Time series; Business and Economics;

    Abstract : Given the importance of copper prices to investors, governments, and policymakers, this paper investigates short-term price predictability using VAR and XGBoost models. All models are trained with historical data from November 2021 to December 2022 and using MSE, RMSE and MAE for evaluating the model performance. READ MORE

  5. 19. Machine Learning of Laser Ultrasonic Data to Predict Material Properties

    University essay from Linköpings universitet/Statistik och maskininlärning

    Author : Filip Tuvenvall; [2023]
    Keywords : Machine Learning; Laser Ultrasonics; Material Properties; Steel; Hardness of Steel;

    Abstract : The hardness of steel is an important quality parameter for several industrial applications. Conventional mechanical testing is used in quality testing for material hardness and the method is time-consuming, can cause material mix-ups, and results in material waste. READ MORE

  6. 20. Segmentation and Valuation in  Stockholm Housing Market : Spatial Continuous and Discontinuous Submarkets Evaluating by Hedonic Price Model and XGBoost Model

    University essay from KTH/Fastighetsekonomi och finans

    Author : Xianglin Sun; [2023]
    Keywords : housing market segmentation ; spatial continuity ; hedonic price model ; XGBoost model ; segmentering av bostadsmarknaden ; rumslig kontinuitet ; hedonisk prismodell ; XGBoost modell ;

    Abstract : The housing market segmentation could provide a reference for more targeted policymaking and investment strategies. Although there have been many studies, there are no consistent submarkets delineating methods because of a lack of theoretical support and subjective evaluation. In this paper, two market segmentation methods are introduced. READ MORE