Essays about: "Model selection"
Showing result 1 - 5 of 1106 essays containing the words Model selection.
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1. Feature Selection for Microarray Data via Stochastic Approximation
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : This thesis explores the challenge of feature selection (FS) in machine learning, which involves reducing the dimensionality of data. The selection of a relevant subset of features from a larger pool has demonstrated its effectiveness in enhancing the performance of various machine learning algorithms. 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. Combining Value Investing with Quality Investing: Empirical Evidence from the European and Nordic Stock Markets
University essay from Handelshögskolan i Stockholm/Institutionen för finansiell ekonomiAbstract : The aim of this thesis is to explore whether stock selection based on five value metrics and six quality metrics can generate superior returns compared to the overall market. The selected markets are the Nordic one (Nasdaq OMX Nordic 120 being the benchmark) and the European one (STOXX Europe 600 being the benchmark), while the selected time period is 2001-2023 for Europe and 2010-2023 for the Nordics. READ MORE
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4. Data Augmentation for Object Detection using Deep Reinforcement Learning
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : Data augmentation is a concept which is used to improve machine learning models for computer vision tasks. It is usually done by firstly, defining a set of functions which transforms images and secondly, applying a random selection of these functions on the images. READ MORE
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5. Regularization Methods and High Dimensional Data: A Comparative Study Based on Frequentist and Bayesian Methods
University essay from Lunds universitet/Statistiska institutionenAbstract : As the amount of high dimensional data becomes increasingly accessible and common, the need for reliable methods to combat problems such as overfitting and multicollinearity increases. Models need to be able to manage large data sets where predictor variables often outnumber the amount of observations. READ MORE
