PERFORMANCE EVALUATION OF LINEAR AND QUADRATIC DISCRIMINANT ANALYSIS MODELS DEPENDING ON DATA DIMENSIONS
Keywords:
Linear Discriminant Analysis, Quadratic Discriminant Analysis, Model Performance ComparisonAbstract
This study aims to evaluate the performance of linear discriminant analysis and quadratic discriminant analysis models on three different data sets containing 100, 500, and 1000 observations. The findings show that LDA is advantageous at low data sizes, but both models perform similarly at medium and large data sizes. It has been observed that LDA shows higher performance, especially with 1000 observations, and that large data sets increase the performance of the models.
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Published
27.12.2023
How to Cite
BULUT, Y. (2023). PERFORMANCE EVALUATION OF LINEAR AND QUADRATIC DISCRIMINANT ANALYSIS MODELS DEPENDING ON DATA DIMENSIONS. JOURNAL OF PURE SOCIAL SCIENCES (PURESOC) - PAK SOSYAL BİLİMLER DERGİSİ (PAKSOS), 4(7). Retrieved from https://paksos.com/index.php/puresoc/article/view/150
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