Essays about: "Slumpmässiga Skogar"

Showing result 1 - 5 of 6 essays containing the words Slumpmässiga Skogar.

1. 1. Evaluating Random Forest and k-Nearest Neighbour Algorithms on Real-Life Data Sets

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

   Author : Atheer Salim; Milad Farahani; [2023]
   Keywords : Random Forest; k-Nearest Neighbour; Evaluation; Machine Learning; Classification; Execution Time; Slumpmässig Skog; k-Närmaste Granne; Utvärdering; Maskininlärning; Klassificiering; Exekveringstid;

   Abstract : Computers can be used to classify various types of data, for example to filter email messages, detect computer viruses, detect diseases, etc. This thesis explores two classification algorithms, random forest and k-nearest neighbour, to understand how accurately and how quickly they classify data. READ MORE

3. 2. Time Series Analysis and Binary Classification in a Car-Sharing Service : Application of data-driven methods for analysing trends, seasonality, residuals and prediction of user demand

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

   Author : Aksel Uhr; [2023]
   Keywords : Smart mobility; Car-sharing; Time series analysis; Demand prediction; Machine learning; Supervised learning; Binary classification; Random forest; Smart mobilitet; Bildelning; Tidsseriaanalys; Efterfrågansprediktering; Maskininlärning; Väglett lärande; Binär klassificering; Slumpmässiga skogar;

   Abstract : Researchers have estimated a 20-percentage point increase in the world’s population residing in urban areas between 2011 and 2050. The increase in denser cities results in opportunities and challenges. Two of the challenges concern sustainability and mobility. READ MORE

4. 3. Learning to Price Apartments in Swedish Cities

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

   Author : Fredrik Segerhammar; [2021]
   Keywords : Real estate; property; machine learning; area data; artificial neural network; random forest; home valuation; price prediction; Fastigheter; maskininlärning; områdesdata; neurala nätverk; slumpmässig skog; hemvärdering; prisförutsägelse;

   Abstract : This thesis tackles the problem of accurately pricing apartments in large Swedish cities using geospatial data. The aim is to determine if geospatial data and population statistics can be used in conjunction with direct apartment data to accurately price apartments in large cities. READ MORE

5. 4. AI Trained to Predict Thresholds of 2D Ellipse Percolation Systems

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

   Author : Nirav Surajlal; [2021]
   Keywords : Low-dimensional Nanomaterials; Percolation Threshold; Machine Learning; Regression; Two-dimensional Heterogeneous Ellipse System; Lågdimensionella nanomaterial; Perkolering Tröskel; Maskininlärning; Regression; Tvådimensionellt Heterogent Ellipssystem;

   Abstract : Percolation theory is a relevant area of research in Nanotechnology because of its wide applications in nanoelectronics based on thin films of nanoparticles and composites, amongst others. In nanotechnology, systems are often explored through modelling and simulations. READ MORE

6. 5. Venn Prediction for Survival Analysis : Experimenting with Survival Data and Venn Predictors

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

   Author : Ignacio Aparicio Vázquez; [2020]
   Keywords : Venn Predictors; Random Forests; Survival Modelling; Machine Learning; Well Calibrated Probabilities; Out-of-bag Calibration; Anomaly Detection; Venn Predictors; Slumpmässiga Skogar; Survival Modelling; Machine Learning; Välkalibrerade sannolikheter; Out-of-bag Calibration; Anomalidetektion;

   Abstract : The goal of this work is to expand the knowledge on the field of Venn Prediction employed with Survival Data. Standard Venn Predictors have been used with Random Forests and binary classification tasks. However, they have not been utilised to predict events with Survival Data nor in combination with Random Survival Forests. READ MORE