<![CDATA[Obesity classification is an important task in health-related data analysis for identifying obesity levels based on individual characteristics. This study aims to implement the Gaussian Naïve Bayes algorithm for obesity level classification and evaluate differences in performance estimates obtained using a 70:30 train-test split and 10-Fold Cross Validation. The dataset consisted of 1,000 observational records containing five predictor variables: age, height, weight, body mass index (BMI), and physical activity level. Obesity levels were categorized into four classes: Underweight, Normal, Overweight, and Obese. The model was implemented using Python and evaluated through two validation scenarios. Experimental results showed that the Gaussian Naïve Bayes model achieved an accuracy of 95.33% using the train-test split approach. Evaluation using 10-Fold Cross Validation produced an average accuracy of 96.30%, precision of 96.66%, recall of 96.45%, and F1-score of 96.48%. These findings indicate that different validation strategies can produce different performance estimates. On the dataset used in this study, Gaussian Naïve Bayes demonstrated high classification performance, while 10-Fold Cross Validation provided performance estimates across multiple data partitions, offering a broader assessment of model performance. This study provides empirical evidence regarding the use of different validation strategies for obesity level classification]]>
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