Essays about: "Random Forest RF"
Showing result 21 - 25 of 79 essays containing the words Random Forest RF.
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21. A Comparative Study on the Effects of Removing the Most Important Feature on Random Forest and Support Vector Machine
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Machine learning (ML) for classification is largely regarded as a “black box”, in that it’s difficult to fully understand how the model reached a decision, and how changes to the input affects the output. Therefore, exploring the inner workings of classification models are of interest for expanding the current knowledge base, providing guidelines for choosing a more suitable classification model for a specific problem. READ MORE
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22. Forest Aboveground Biomass Monitoring in Southern Sweden Using Random Forest Modelwith Sentinel-1, Sentinel-2, and LiDAR Data
University essay from Högskolan i Gävle/SamhällsbyggnadAbstract : Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. READ MORE
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23. Nowcasting U.S. inflation using mixed frequency real-time data
University essay from Lunds universitet/Matematisk statistikAbstract : Different models were developed with the aim of nowcasting inflation at a daily basis with high frequency variables, while using real-time data to avoid look ahead bias. Both popular machine learning models such as Random Forest and XGBoost, and more traditional models such as UMIDAS and Almon distributed lag models were used to make the nowcasts. READ MORE
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24. Predicting the size of a company winning a procurement: an evaluation study of three classification models
University essay from Uppsala universitet/Statistiska institutionenAbstract : In this thesis, the performance of the classification methods Linear Discriminant Analysis (LDA), Random Forests (RF), and Support Vector Machines (SVM) are compared using procurement data to predict what size company will win a procurement. This is useful information for companies, since bidding on a procurement takes time and resources, which they can save if they know their chances of winning are low. READ MORE
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25. Spent Nuclear Fuel under Repository Conditions : Update and Expansion of Database and Development of Machine Learning Models
University essay from KTH/KemiAbstract : Förbrukat kärnbränsle är mycket radioaktivt och behöver därför lagras i djupa geologiska förvar i tusentals år innan det säkert kan återföras till naturen. På grund av de långa lagringsperioderna görs säkerhetsanalyser av de djupa geologiska förvaren. READ MORE
