Essays about: "voting ensemble model"
Showing result 1 - 5 of 6 essays containing the words voting ensemble model.
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1. Stock market analysis with a Markovian approach: Properties and prediction of OMXS30
University essay from KTH/Matematisk statistikAbstract : This paper investigates how Markov chain modelling can be applied to the Swedish stock index OMXS30. The investigation is two-fold. Firstly, a Markov chain is based on index data from recent years, where properties such as transition matrix, stationary distribution and hitting time are studied. READ MORE
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2. Credit Card Approval Prediction : A comparative analysis between logistic regressionclassifier, random forest classifier, support vectorclassifier with ensemble bagging classifier.
University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskapAbstract : Background. Due to an increasing number of credit card defaulters, companies arenow taking greater precautions when approving credit applications. When a customermeets certain requirements, credit card firms typically use their experience todecide whether to grant them a credit card. READ MORE
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3. Stronger Together? An Ensemble of CNNs for Deepfakes Detection
University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Abstract : Deepfakes technology is a face swap technique that enables anyone to replace faces in a video, with highly realistic results. Despite its usefulness, if used maliciously, this technique can have a significant impact on society, for instance, through the spreading of fake news or cyberbullying. READ MORE
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4. Ensemble approach to code smell identification : Evaluating ensemble machine learning techniques to identify code smells within a software system
University essay from Jönköping University/JTH, Datateknik och informatikAbstract : The need for automated methods for identifying refactoring items is prelevent in many software projects today. Symptoms of refactoring needs is the concept of code smells within a software system. Recent studies have used single model machine learning to combat this issue. READ MORE
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5. High-risk Consumer Credit Scoring using Machine Learning Classification
University essay from Lunds universitet/Matematisk statistikAbstract : The use of statistical models in credit rating and application scorecard modelling is a thoroughly explored field within the financial sector and a central component in a credit institution’s underlying business model. The aim of this report was to apply and compare six different machine learning models in predicting credit defaults for high-risk consumer credits, using a data set provided by a Swedish consumer credit institute. READ MORE