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Visar resultat 1 - 5 av 44 uppsatser som matchar ovanstående sökkriterier.

1. 1. Decision Trees for Classification of Repeated Measurements

   Kandidat-uppsats, Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakulteten

   Författare :Julianna Holmberg; [2024]
   Nyckelord :Repeated Measurement Data; Growth Curve Model; Linear Discriminant Analysis; Decision Tree; Bootstrap Aggregating; CART; CART-LC;

   Sammanfattning : Classification of data from repeated measurements is useful in various disciplines, for example that of medicine. This thesis explores how classification trees (CART) can be used for classifying repeated measures data. LÄS MER

3. 2. Robustness Against Non-Normality : Evaluating LDA and QDA in Simulated Settings Using Multivariate Non-Normal Distributions

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Gånheim Viktor; Åslund Isak; [2023]
   Nyckelord :Classification; Linear Discriminant Analysis; Quadratic Discriminant Analysis; Normality Assumption;

   Sammanfattning : Evaluating classifiers in controlled settings is essential for empirical applications, as extensive knowledge on model-behaviour is needed for accurate predictions. This thesis investigates robustness against non-normality of two prominent classifiers, LDA and QDA. LÄS MER

4. 3. Predicting the size of a company winning a procurement: an evaluation study of three classification models

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Ellen Björkegren; [2022]
   Nyckelord :public procurement; classification; Linear Discriminant Analysis; Random Forests; Support Vector Machines;

   Sammanfattning : 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. LÄS MER

5. 4. Predicting the outcome of IVF treatments using forward selection regression and linear discriminant analysis

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Agnes Ekman; Linnéa Fahlberg; [2022]
   Nyckelord :Mathematics and Statistics;

   Sammanfattning : As more and more In Vitro Fertilizaiton (IVF) treatments are performed each year, there is a need to better predict the outcomes of different stages of the treatment and hence get a better understanding of which hormonal and physical parameters affect the treatment outcome and in what way. In this study, the effect of interaction between baseline AMH (anti-mullerian hormone) and DFI (DNA fragmentation index) on the chance of obtaining at least one good quality embryo was investigated, but no significance was found. LÄS MER

7. 5. Predicting basketball performance based on draft pick : A classification analysis 

   Kandidat-uppsats, Uppsala universitet/Statistiska institutionen

   Författare :Fredrik Harmén; [2022]
   Nyckelord :machine learning; linear discriminant analysis; k-nearest neighbors; support vector machines; random forests;

   Sammanfattning : In this thesis, we will look to predict the performance of a basketball player coming into the NBA depending on where the player was picked in the NBA draft. This will be done by testing different machine learning models on data from the previous 35 NBA drafts and then comparing the models in order to see which model had the highest accuracy of classification. LÄS MER